Physics/AI Jobs

As a hub for the intersection of Physics and AI in the Boston area and beyond, we are happy to share job opportunities at this intersection as we become aware of them.

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IAIFI Jobs

The following positions are at IAIFI-affiliated universities and therefore have the potential for IAIFI involvement; they are not necessarily directly hired by IAIFI.

Faculty Opportunities

Postdoc Opportunities

Postdoctoral researchers at any of the partner institutions may collaborate with IAIFI researchers to become a Junior Investigator.

Graduate Student Opportunities

IAIFI does not have a dedicated PhD program, but PhD students at any of the partner institutions may collaborate with IAIFI researchers to become a Junior Investigator.

Other Opportunities

Academic Opportunities

Faculty Opportunities

Assistant or Associate Professor in Computational Science and Technology

The Cyprus Institute, Aglandjia, Cyprus
Deadline: 2025-04-15 | Apply

Details

The Cyprus Institute invites applications for a new faculty member at the level of Assistant or Associate Professor in the broader area of Computational Science and Technology, to join the Institute’s CaSToRC Centre on a full-time basis (100% FTE) for its premises at Athalassas’ Campus.

Computational Science and Technology is a well-established research area at CyI, and it includes experts in Machine Learning, Computational Modelling, and High-Performance Computing.

Assistant Professor (W1) of Explainable Artificial Intelligence (W1 with Tenure Track to W3)

Ulm University, Germany
Deadline: 2025-04-23 | Apply

Details

The Faculty of Engineering, Computer Science and Psychology, Institute of Artificial Intelligence, is seeking to fill the position, starting as soon as possible, of an Assistant Professor (W1) of Explainable Artificial Intelligence (W1 with Tenure Track to W3); (m/f/d)

The research focus of the position is on fundamentals and methods for explainable artificial intelligence. Relevant topics include:
-Explainability of black-box models (e.g. neural networks)
-Interpretability of generative AI models
-Integration and extraction of knowledge in AI systems
-Neuro-symbolic AI systems
-Multimodal clarification dialogues

Endowed Chair in Computer Science, Associate or Full Professor

Stevens Institute of Technology, Hoboken, New Jersey
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Details

The Department of Computer Science (CS) in the Charles V. Schaefer, Jr. School of Engineering and Science (SES) at Stevens Institute of Technology (Stevens) invites applications for a tenured endowed chair position at the rank of associate or full professor. Our priority areas for hiring are existing focus areas in the department (AI/ML/vision/NLP; cybersecurity and privacy; systems and programming languages), as well as areas we plan to expand (data science; human-centric computing). However, exceptional candidates in all areas of Computer Science may be considered.

Ad Astra Fellow - Assistant Professor in Foundational Technologies and Models for Artificial Intelligence (AI) and Machine Learning (ML) (Job ID 18112)

University College Dublin (UCD), Ireland
Deadline: 2025-02-21 | Apply

Details

The UCD School of Computer Science (SCS) and School Mathematics and Statistics (SMS) is seeking to appoint an Assistant Professor in the area of foundational technologies and models for Artificial Intelligence (AI) and Machine Learning (ML). The successful candidate will develop foundational machine learning, algorithms and architectures, to support advances in the application of Artificial Intelligence.

Ad Astra Fellow - Assistant Professor in Computer Science in the area of Human AI Collaboration (Job ID 18111)

University College of Dublin (UCD), Ireland
Deadline: 2025-02-21 | Apply

Details

The UCD School of Computer Science is seeking to recruit an assistant professor in the area of Human AI Collaboration. With the advent of Generative Artificial Intelligence (GenAI) and, especially, Large Language Models (LLMs) there is a great debate in academia, industry and society about the extent to which Artificial Intelligence (AI) may equal or surpass Human Intelligence. This debate informs our understanding of the differences between Human Intelligence and AI and also informs research on Human AI Collaboration. As AI increasingly supports humans in performing different tasks, from improving medical diagnostics to vehicle navigation, harnessing the strategic partnership between human intelligence and AI technology is crucial.

Postdoc Opportunities

Research positions (collaborative agentic intelligence, data-centric optimization, + more)

Learning and INference Systems Laboratory (LINs Lab) at Westlake University, Hangzhou, China
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Details

The Learning and INference Systems Laboratory (LINs Lab, https://lins-lab.github.io) at Westlake University has multiple research positions available.

The lab is currently recruiting researchers (Ph.D. students, visiting students, research assistants, and postdoctoral fellows) in the following research areas:
- Collaborative agentic intelligence;
- Data-centric optimization for deep learning;
- Foundation and application of generative models.

Humboldt Postdoctoral Fellowships

Collaborative Artificial Intelligence (CAI) group at the University of Stuttgart in Germany
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Details

The Collaborative Artificial Intelligence (CAI) group at the University of Stuttgart in Germany, headed by Prof. Andreas Bulling, is inviting applications for several prestigious Humboldt Postdoctoral Fellowships.

The CAI group (https://collaborative-ai.org/) conducts fundamental research towards collaborative artificial intelligence at the intersection of multimodal machine learning, multi-agent reinforcement learning, computational cognitive modelling, computer vision, and human-AI interaction. We have a strong presence at the top conferences in these research fields and, as such, are among the few truly interdisciplinary groups in Germany and Europe. We are highly visible internationally, and publications from the group are frequently distinguished with best paper awards. So far, out of 14 PhD and PostDoc alumni, seven have assumed faculty positions at leading institutions worldwide, several others have jointed industrial research labs. Close links exist to the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and the local Max Planck Institute, the ELLIS Unit Stuttgart, the Centre for Bionic Intelligence Tübingen Stuttgart (BITS), as well as several large-scale research projects within the University of Stuttgart.

We are seeking outstanding candidates with an exceptional research track record and strong publications at the top venues in the above fields. Successful candidates will be able to pursue their own research program, will contribute to the preparation and leadership of research projects, will have the opportunity to advise undergraduate and graduate students, and contribute to the teaching activities of the group. They will have access to excellent research infrastructure, including distinguished lectures, research seminars, and invited talks by international guests, extensive technical equipment (e.g. eye and fully body motion trackers, various wearable physiological sensors, VR/AR headsets), as well as our own high-performance GPU cluster.

ABOUT THE HUMBOLDT FELLOWSHIPS

The Postdoctoral Fellowships are competitive, fully funded for two years and, upon successful nomination, granted directly by the Alexander von Humboldt Foundation after only a formal check. This light-weight process provides a unique, fast-track option for obtaining a postdoctoral research position as it allows successful candidates to start working in our group within only a few weeks (not including any visa application process, if required). In addition to the fellowship amount itself, a mobility allowance, a subsidy towards the cost of medical and liability insurance, and a lump sum for travel expenses and an initial lump sum are paid. A family allowance for accompanying partners and/or children under 18 will be granted if they are staying in Germany for at least three months. Intensive German language courses are available. Fellows will become part of the world-wide Humboldt Research Network, making them eligible for various forms of alumni sponsorship upon successful completion of the research stay.

The Alexander von Humboldt Foundation wants to promote diversity and, thus, candidates from under-represented groups and female junior researchers from abroad are particularly encouraged to apply.

Important formal criteria include:
- Doctorate completed within the last 4 years
- Above-average publication record, commensurate with career level
- No previous post-doctoral research stay nor completed degrees/doctorates in Germany
- No German nationality
- Very good knowledge of English
- No previous application to the Humboldt Foundation; no previous sponsorship in any of the Foundation’s fellowship programmes.

ABOUT THE UNIVERSITY OF STUTTGART

The University of Stuttgart is cradled in what is simultaneously one of Germany's most beautiful landscapes and one of Europe's most economically successful areas. The region is known for a high standard of living, beautiful surroundings, and easy access to other major metropolitan areas. The university is one of the top nine leading and oldest technical universities (TU9) in Germany and consistently ranked among the world's best universities in international rankings. The University of Stuttgart is part of the Cyber Valley initiative (https://cyber-valley.de/en), a new center for artificial intelligence research that brings together partners from science and industry to boost intelligent systems research and development in the Stuttgart-Tübingen region, specifically in the areas of machine learning, robotics, and computer vision.

The Department of Computer Science is located on the university campus in Stuttgart Vaihingen. It is devoted to cutting-edge research in computer science ranging from foundations (algorithms, programming logics, computer architecture) to a variety of application domains (computer vision and graphics, machine learning and robotics, intelligent systems). With its vibrant research environment and its attractive Bachelor and Master programs, the Department attracts top students from all over the world. The department offers a stimulating, competitive, and collaborative work environment. It is equipped with high-class research facilities and has links to leading international companies in and around Stuttgart (e.g. Bosch AI, Mercedes-Benz, Porsche).

APPLICATIONS

Applications and inquiries should be sent to Prof. Dr. Andreas Bulling (see contact details below). Applications must be submitted by email as a single pdf (max. 10 MB) and include a CV, motivation letter with research statement, publication list, transcripts of BSc and MSc degrees, and contact details of 2-3 references. Optionally, up to five selected own publications or theses can be included in a second pdf (max. 5 MB). Applications should also indicate earliest date of availability. There is no fixed application deadline; applications are continuously considered as positions become available, staggered over a three-year timeframe from 2025-2027.

--
Prof. Dr. Andreas Bulling
Full Professor
Collaborative Artificial Intelligence

Executive Board, International Max-Planck Research School for Intelligent Systems (IMPRS-IS)

Founding Director, Stuttgart ELLIS Unit

ELLIS Fellow

Institute for Visualisation and Interactive Systems (VIS)
University of Stuttgart
Pfaffenwaldring 5a, 70569 Stuttgart, Germany

Email: andreas.bulling@vis.uni-stuttgart.de

URL: https://collaborative-ai.org/people/bulling/

Research Fellow (LLM)

The National University of Singapore
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Details

The National University of Singapore invites applications for the position of Research Fellow in the Department of Computer Science, School of Computing (SoC). SoC is strongly committed to research excellence in all its dimensions: Searching for fundamental results and insights, developing novel computational solutions to a wide range of applications, building large-scale experimental systems and improving the well-being of society. We seek to play an active role both internationally and locally in the core and emerging areas of Computer Science and Information Systems.

Research Fellow (Uncertainty Quantification in LLMs)

Nanyang Technological University, Singapore
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Details

Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a leading college that is known for its excellent curriculum, outstanding and impactful research, and world-renowned faculty. Today, we are ranked #2 for AI and Computer Science by US News Best Global Universities; and #8 for Data Science and AI by QS World University Ranking.

A hot bed of cutting-edge technology and groundbreaking research, the College aims to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow'. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing.

We seek to appoint a postdoctoral Research Fellow (fixed term, 30 months) to work on uncertainty quantification in LLMs.

Senior Postdoctoral Research Fellow

University of Oxford, England
Deadline: 2025-02-26 | Apply

Details

We invite applications for a Senior Postdoctoral Research Fellow to join the Oxford Computational Statistics and Machine Learning (OxCSML) research group, with responsibility for leading and carrying out research pertinent to the project, as well as day-to-day management of research activities relevant to the project, e.g., weekly meetings, journal clubs, seminars etc. The post holder will be reporting to the Principal Investigator Prof Yee-Whye Teh.

Integreat/University of Oslo - Various Postdoc Announcements

University of Oslo, Norway
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Details

University of Oslo/Centre of excellence on knowledge-driven machine learning Integreat is looking for candidates –
This call is part of the Marie Skłodowska-Curie Actions/COFUND – see the mobility requirement.

Worth noticing:
- Mobility requirement: candidates - not have resided or carried out main activity in Norway for more than 12 months in the 3 years immediately prior to 6 April!
- Candidates co-write their applications with mentors (point of contacts for the position) – in the call.
- Full positions 3 years
- Starting date no later than 1 October 2025
- Doctoral dissertation must be submitted and successfully defended by 6 April 2025
- Full-time research (no possibility for part-time)
- Moving to Norway
- Work placement: University of Oslo, Integreat

Integreat highlighted projects

Double descent and sufficiency: https://www.uio.no/dscience/english/dstrain/research-areas2025/mathematics---statistics/double-descent-and-sufficiency/index.html
Mathematics - Point of contact: Emil Aas Stoltenberg

Separation of linguistic and factual knowledge in Large Language Models (LTG2): https://www.uio.no/dscience/english/dstrain/research-areas2025/informatics/understanding-large-language-models/index.html
Informatics - Point of contact: Andrey Kutuzov

Neural Networks Learning via Sufficiency and Information Theory (DSB4-A): https://www.uio.no/dscience/english/dstrain/research-areas2025/informatics/machine-learning-signal-processing-and-image-analy/index.html
Informatics - Point of contact: Ali Ramezani-Kebrya

Sustainable, Efficient, and Secure Machine Learning (DSB4-B): https://www.uio.no/dscience/english/dstrain/research-areas2025/informatics/machine-learning-signal-processing-and-image-analy/index.html
Informatics - Point of contact: Ali Ramezani-Kebrya

Joint Physics-informed, Data-driven, and Digital twins for Complex Dynamical System Solvers (DSB4-C): https://www.uio.no/dscience/english/dstrain/research-areas2025/informatics/machine-learning-signal-processing-and-image-analy/index.html
Informatics - Point of contact: Ali Ramezani-Kebrya

PhD and postdoc positions in Learning high-dimensional (networked) dynamical systems

NTU Singapore
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Details

Professor Hemant Tyagi will have multiple openings for PhD and postdoc positions in 2025 as part of a research group that is in the process of being created at NTU Singapore (in the School of Physical and Mathematical Sciences).

The positions pertain to the research topic 'Learning high-dimensional (networked) dynamical systems' (broadly defined) and there is ample room for exploring sub-topics depending on the interest of the candidate. The focus will be on deriving efficient algorithms with provable statistical guarantees, using tools from: high-dimensional statistics, optimization, probability theory, approximation theory etc.

DSTrain - MSCA Postdoctoral Fellowships in Computational and Natural Sciences

University of Oslo, Norway
Deadline: 2025-04-06 | Apply

Details

The DSTrain programme will facilitate ground-breaking research by emphasising interaction between data science, computational science, natural sciences, and technology. The postdoctoral fellows will be integrated in research groups across scientific disciplines where there is an urgent need for computationally skilled researchers. The research groups provide excellent research environments and supervision enabling world-class research, innovation and digital infrastructure, models for interdisciplinary collaboration and training in research, including computational and transferable skills. The DSTrain program also includes a range of cross-sectorial secondments opportunities.

CHAI Research Fellow

University College London, England
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Details

The Department of Statistical Science at UCL is the longest established university statistics department in the world and has played a pioneering role in the development of the subject since its foundation in 1911. It is one of nine departments in the UCL Faculty of Mathematical and Physical Sciences and has close links with many other departments, both within the Faculty and outside of it. We teach statistical science at all levels (undergraduate single/combined honours, service courses, MSc and PhD) and carry out research across a wide range of theoretical and applied areas. In the last Research Excellence Framework exercise (2021/22), over 97% of our output was classified as “world-leading” or “internationally excellent” in terms of originality, significance and rigour. We consistently score highly in the National Student Survey in the Postgraduate Taught Experience Survey.

Vector Distinguished Postdoctoral Fellow

The Vector Institute, Toronto, Canada
Deadline: 2025-02-28 | Apply

Details

Are you an early career researcher with the potential to become a world-class machine learning scientist? The Vector Institute invites applications for Distinguished Postdoctoral Fellows who are working on cutting-edge fundamental research in machine learning and deep learning algorithms and their applications.

Postdoctoral Scholar | College of IST Data Sciences and Artificial Intelligence

Penn State, University Park, Pennsylvania
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Details

The Data Sciences and Artificial Intelligence (DS/AI) group at Penn State invites applications for a Postdoctoral Scholar position, set to commence immediately. This role is centered on cutting-edge research at the nexus of machine learning, deep learning, computer vision, psychology, and biology, with foci on psychology-inspired AI and addressing significant biological questions using AI.

Postdoctoral Position - Automatic selection of predictive algorithms by meta-learning for time series forecasting

LIFO (Laboratoire d’Informatique Fondamentale d’Orléans), Orléans, France
Deadline: 2025-03-15 | Apply

Details

Profile: PhD in machine learning (computer science or applied mathematics)
Duration: 1 year contract
Affiliation: LIFO (Laboratoire d’Informatique Fondamentale d’Orléans) - Constraints and Machine learning (CA) team.
Gross salary: around 2600€/month
The deadline to apply is 15th March 2025
Supervisor: Marcílio de Souto (marcilio.desouto@univ-orleans.fr)

Skills
- Good experience in data analysis and machine learning is required.
- Experiences/knowledge in time series prediction and environmental science are welcome.
- Curiosity and ability to communicate (in English or French) and to work in collaboration with scientists in environmental science.
- Ability to propose and validate new solutions and to publish the results.
- Autonomy and good organizational skills.

How to candidate
Candidates are invited to send a pdf file to Marcílio de Souto (marcilio.desouto@univ-orleans.fr) that contains:
- A short CV, with descriptions of your thesis and experiences in machine learning, including deep learning (including projects you were involved in)
- A motivation letter
- contact information for two references
- Deadline for submission of application: March 15th, 2025.

Context
The JUNON project is granted from the Centre-Val de Loire region through an ARD program (Ambition Recherche Développement). The project is led by BRGM (Bureau de Recherches Géologiques et Minières) and involvesUniversity of Orléans (LIFO), University of Tours (LIFAT), CNRS, INRAE, ATOS and ANTEA companies. The main goal of JUNON is to develop digital twins to improve the monitoring, understanding and prediction of environmental resources evolution and phenomena, for a better management of natural resources. Digital twins will allow us to virtually reproduce natural processes and phenomena using combinations of AI and environmental tools. They will rely on geological and meteorological data (time series) and knowledge, as well as physical-based models.

JUNON project is organized into 5 work packages (WP):

1. User’s needs and geological knowledge for ground water
2. User’s needs and biological/chemical knowledge about pollutants and greenhouse gases
3. Data management and data mining
4. Times series predictions
5. Aggregation and realization of digital twins

The postdoc program will be supervised by LIFO-CA and will be in WP4, focusing on time series forecasting. There will be strong interactions inside WP4 with other postdocs and PhD in LIFO or LIFAT, with WP1 and WP3 (BRGM) with engineers. The CA team is a dynamic team with 9 PhD students. We work on Machine Learning, Data Mining and Deep Learning and are interested, among other things, in knowledge integration and explicability in ML/DM methods.

Objectives

In many domains, various algorithms can be considered candidates for solving particular problems. One of the most challenging tasks is to predict when one algorithm is better than another for solving a given problem. Traditional approaches to predicting algorithm performance often involve costly trial-and-error procedures. Other approaches require specialized knowledge, which is not always easy to acquire.

Meta-learning approaches have emerged as effective solutions, capable of automatically predicting algorithm performance for a given problem (Bradzil et al., 2022;Vanschoren, 2019). Thus, such approaches could help non-expert users in the algorithm selection task. There are different interpretations of the term “meta-learning”. Here we use the term “meta-learning” to refer to the automatic knowledge generation process that relates the performance of algorithms – in particular machine learning and data preprocessing techniques – to the characteristics of the problem (i.e., the characteristics of its datasets).

As an automatic algorithm selection technique, meta-learning does not imply being limited to machine learning algorithms. Therefore, the application of this approach to 'classical' predictive models is also envisaged. This typically requires the intervention of experts to parameterize these models in order to build the set of metadata necessary for the 'meta-learner'. The BRGM in particular and, more broadly, the consortium of this proposal, has many forces capable of parameterizing these different models (empirical, physical or statistical), thus opening the scope to all environmental predictive techniques.

By using meta-learning, our objective is therefore to provide a framework for linking a set of time-series data representing an environmental problem, possibly associated with a priori knowledge, with a pipeline of data mining algorithms (e.g., preprocessing and supervised learning algorithms). In particular, it will aim to give environmental experts a certain autonomy, in the context of the construction of digital twins, and thus limit their dependence on digital experts on this issue (Garcia et al., 2018; Talkhi et al., 2024).

DDSA Postdoc Fellowship Call 2025

Danish Data Science Academy (DDSA)
Deadline: 2025-03-05 | Apply

Details

Danish Data Science Academy (DDSA) invites applications for six two-year postdoctoral fellowships of DKK 1,300,000 (+ 5% administrative costs) to individual research projects to support visionary and creative-thinking young researchers pursuing their own research ideas in collaboration with a strong host environment at a Danish research institution.

Other Academic Opportunities

Research Engineer II (Computer Science/Statistics/Engineering)

Nanyang Technological University, Singapore
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Details

Research Software Engineer

University of Oxford, England
Deadline: 2025-02-26 | Apply

Details

We invite applications for a Research Software Engineer to join Oxford Computational Statistics and Machine Learning (OxCSML) research group, with responsibility for managing the computational infrastructure, enabling large scale research implementation and experimentation for the project team, and collaborating on research pertinent to the project. The post holder will be reporting to the Principal Investigator Prof. Yee-Whye Teh.

Industry Opportunities

Industrial Metaverse Intern

Nokia Bell Labs, Murray Hill, New Jersey
Deadline: 2025-04-04 | Apply

Details

The successful applicants will work with our current team to conduct high-quality fundamental research and apply the findings to problems that arise in a wide range of Nokia applications. Likely focus areas include:

-3D modeling and visual computing
-Systems for industrial monitoring and analytics

Research Scientist – Generative AI

Nokia Bell Labs, Murray Hill, New Jersey
Deadline: 2025-06-13 | Apply

Details

Your focus will be on Generative AI, Large Language Models (LLMs), multi-modal AI, AI reasoning, and AI applications in robotics within a dynamic industrial research environment. This role offers the opportunity to develop and prototype innovative systems, showcase your research breakthroughs, and publish your findings in leading conferences and journals.

Bell Labs Machine Learning and AI Intern

Nokia Bell Labs, Murray Hill, New Jersey
Deadline: 2025-04-04 | Apply

Details

As a Machine Learning and AI Intern student at Nokia Bell Labs, you will be a part of the Statistics and Data Science Research Group, working on Machine Learning and AI projects with a focus on Natural Language Processing (NLP), Large Language Models (LLMs), Computer Vision, Generative AI, and AI in robotics in an exciting industrial research environment. This role will give you the opportunity to gain valuable skills and experience in a dynamic team. You will have the opportunity to build and prototype systems to demonstrate research innovations and publish research in premier conferences and journals.

Applied AI/ML Research Solutions Intern

Nokia Bell Labs, Murray Hill, New Jersey
Deadline: 2025-04-04 | Apply

Details

Nokia Bell Labs is Nokia's world-renowned research arm. It has invented many of the foundational technologies that underpin information and communications networks and all digital devices and systems. This research has produced nine Nobel Prizes, five Turing Awards, and numerous other awards.

The AI Accelerator Group at Nokia Bell Labs is developing state-of-the-art artificial intelligence-based solutions focused on using computer vision and natural language processing to understand physical spaces in space-time, exploring generalizable learning models and optimization techniques for real-time performance.

Software Engineer, Foundation Models for Science (AI/Machine Learning)

Flatiron Institute - Simons Foundation, New York, New York
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Details

The Simons Foundation’s Flatiron Institute seeks a full-time research software engineer position as part of its new initiative, Polymathic AI, Building Foundation Models for Science. Recent advances in machine learning, including Large Language Models and diffusion-based generative models have driven significant advances in Artificial Intelligence. The Flatiron Institute seeks to leverage these ideas to develop the next generation of AI-powered scientific analysis tools, seeking more powerful and more interpretable models with broad applications to many scientific disciplines. Our group includes researchers from multiple scientific disciplines ranging from machine learning to astrophysics, biology, neuroscience, and quantum computing.

Senior Machine Learning Engineer

Climate X, London, England
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Details

As a Senior Machine Learning Engineer at Climate X, you will join an interdisciplinary team of other Data Scientists, Climate Scientists and Geospatial experts, collaborating closely with our Engineering and Product teams to deliver impactful products to our clients.

This role will support our NLP model, a core product within the business. This will involve developing an existing code base, research time for exploring new techniques and algorithms, fine-tuning LLM models on domain-specific datasets to enhance the performance of our existing model, perform statistical analysis and techniques for model evaluation, analyse text data to extract meaningful insights and trends and create visualizations to communicate findings and facilitate understanding of the model across the business and our clients.