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.

IAIFI Fellow

IAIFI/MIT
Deadline: 2025-10-08 | Apply

Details

To facilitate advances in AI+Physics, the IAIFI seeks a talented and interdisciplinary group of researchers at an early stage of their careers to join the IAIFI Fellowship program. The role of an IAIFI Fellow is to spark vital interdisciplinary, multi-investigator, multi-subfield collaborations across the primary IAIFI domains of theoretical physics, experimental physics, astrophysics, and foundational AI. Such collaborations have immense power to generate new ideas and approaches in both physics and AI, to facilitate abstracting physics challenges beyond their native domains to inform the development of cutting-edge AI tools, and to instill a common language across disciplines. Our program aims to appoint new postdoctoral IAIFI Fellows each academic year, for a three-year fellowship term each.

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

University Assistant Professor in Machine Learning

University of Cambridge, England
Deadline: 2025-09-22 | Apply

Details

Applications are invited for a University Assistant Professorship in the broad area of Machine Learning. The successful candidate will join the Computational and Biological Learning Lab (CBL) cbl.eng.cam.ac.uk in the Information Engineering Division. CBL combines expertise in machine learning with computational neuroscience. The candidate will lead a research programme in one or more of the following areas: machine learning, decision making, and theory and practice of deep learning.

Open Rank Faculty Position (Tenured or Tenure-Track) in Artificial Intelligence

The Chinese University of Hong Kong, Shenzhen, China
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Details

Newly established, The School of Artificial Intelligence (SAI) at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) is pleased to invite applications for full-time faculty positions at all academic ranks with multiple positions. We seek outstanding individuals who are eager to contribute to the advancement of artificial intelligence research and education. Candidates will join a vibrant, interdisciplinary environment that values academic excellence, innovative research, and impactful teaching.

Faculty Position on the Artificial Intelligence & Machine Learning (AIML) team

TCG CREST, Kolkata, West Bengal, India
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Details

The Artificial Intelligence & Machine Learning (AIML) team at TCG CREST is seeking exceptional faculty members (Assistant/Associate/Full Professors) to lead pioneering research and innovation.

We are committed to fostering a diverse, inclusive, and equitable environment and strongly encourage applications from candidates of all backgrounds—including race, gender, demographics, and individuals with special abilities.

What We’re Looking For:

- Expertise in cutting-edge areas such as (but not limited to) reinforcement learning, computational learning theory, computer vision, and natural language processing.
- Proven research excellence, with publications in top journals (e.g., JMLR, IEEE T-PAMI, T-IFS, T-IT, T-IP, T-ASLPRO, JACM) and conferences (Core A*/A venues).
- Strong research leadership, including experience as PI or Co-PI on externally funded projects.
- Teaching experience at premier institutions (e.g., older IITs, IISc, TIFR) or globally recognized universities, and/or research experience at top institutes or companies.
- A collaborative and ethical mindset, with integrity, academic rigor, and the ability to work across diverse projects.
- High motivation to contribute to societal development and nation-building in India.

How to Apply:
- For queries and application submissions, please send your CV and cover letter to: goutam.mukherjee[at]tcgcrest.org

W2 Professorship (W3 tenure track position) in Language Technology

Saarland University, Saarbrücken, Germany
Deadline: 2025-09-12 | Apply

Details

We are seeking an internationally recognized researcher in the field of language technology with extensive expertise in natural language processing and machine learning methodologies. Particular interest will be given to candidates with experience in training, analysing, developing and improving large-scale neural architectures. In addition to holding a professorship at the university, the successful candidate will also be appointed as a scientific director at the German Research Center for Artificial Intelligence (DFKI), where they will head a research department with access to an extensive global network of industry and other partners. This dual role offers a unique platform for high-impact research and meaningful societal engagement at a scale rarely achievable elsewhere. DFKI is an application-driven research organization that is largely financed through external project funding. Experience in collaborating with non-academic partners and in attracting national and international research funding is therefore desirable.

Tenure-Track Professorship in Human Computer Interaction

The University of Vienna, Austria
Deadline: 2025-09-17 | Apply

Details

Human Computer Interaction (HCI) explores the design, evaluation, and implementation of interactive computing systems for human use, as well as the broader social and ethical implications of computer systems. Candidates should have a strong background in computer science or a related discipline and an excellent research record in HCI, as documented by publications in relevant top venues (such as ACM CHI, ACM UIST, CSCW, TOCHI, IEEE VIS, or IEEE TSMC) and by successful acquisition of research grants. We are also welcoming candidates with demonstrated interest and experience in inter- and transdisciplinary research in HCI. Relevant research focus areas include, but are not limited to: Human-centered design and user experience, interactive and intelligent systems, critical computing, sustainability and social justice, collaborative and social computing, mixed, augmented, and virtual reality, affective computing and human-AI interaction, and ethical, societal, artistic and environmental aspects of digital technologies. Candidates should be committed to high-quality teaching in HCI and other general areas in computer science.

Postdoc Opportunities

Postdoc in AI for fundamental physics (theory or statistical inference / experiment)

Georgia Tech., Atlanta, Georgia
Deadline: 2025-12-05 | Apply

Details

The successful candidate will work on one of these research themes and should have relevant expertise.
- For theory-focused projects, a strong background in group theory, quantum field theory, or gravity, as well as familiarity with relevant physics software, is recommended.
- For statistical-inference-focused or experiment-design-focused projects, a strong background in statistics and/or particle physics is recommended.
The postdoc will also have the option to work within an experiment collaboration (eg. CMS or DUNE).

Postdoctoral Fellow in Machine Learning

Nordita (The Nordic Institute for Theoretical Physics), Stockholm, Sweden
Deadline: 2025-12-31 | Apply

Details

The candidate will work on problems at the intersection of mathematical statistics, machine learning, and generative modeling, particularly for sequential data arising in complex dynamical systems. The candidate is expected to carry out research and publish their findings in top conferences and journals, and help in the management of the group, as well as supervision of students on research projects. The position is meant to further the careers of early-career researchers who wish to pursue a career in academia. The job description may be changed during the contract period or upon contract renewal.

Two full-time academic positions - Theory and applications in the field of data science and AI

University of Chile, Santiago, Chile
Deadline: 2025-11-10 | Apply

Details

The Faculty of Physical and Mathematical Sciences (FCFM) at the University of Chile invites applications for two full-time academic positions (44 hours per week), jointly appointed by the Institute for Data and Artificial Intelligence (IDIA). These positions are in the Theory and Applications of Data Science and Artificial Intelligence. Successful candidates are expected to engage in both individual and collaborative research, contribute to undergraduate and graduate teaching (specially with the Master of Data Science), and participate in outreach activities. They will also have opportunities to develop industry connections through research and development projects, as well as involvement in postgraduate courses or diploma programs.

Postdoctoral Research Assistant in Learning Perceptive Contact-Rich Loco-Manipulation

University of Oxford, England
Deadline: 2025-09-01 | Apply

Details

We are seeking a full-time Postdoctoral Research Assistant to join the Dynamic Robot Systems Group of the Oxford Robotics Institute and the Department of Engineering Science in central Oxford. The post is funded by the EPSRC’s Centre to Centre (C2C) Grant, “Mobile Robotic Inspector: Learning to Explore and Manipulate in the Real World” and is fixed-term of 24 months duration.

Postdoc Application for Learn2Opt - Learning to Optimize - A Sustainable Approach

ESSEC Business School, Paris, France
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Details

Project Title: Learn2Opt — Learning to Optimize: A Sustainable Approach

Host Institution: ESSEC Business School
Location: Paris Area, France
Starting Date: September–November 2025 (flexible)
Duration: 18 months (1.5 years), with a possible extension of up to 1 additional year

Project Summary:
The proposed research aims to develop innovative, efficient, and sustainable techniques for optimization enhanced by machine learning (ML), focusing on decomposition methods such as column generation and Benders decomposition. These methods are essential for solving complex large-scale decision-making problems, but their high computational demands often limit their practical application. A major challenge in current methodological evaluations is the neglect of the time and effort required to train ML models. In practice, training these models can require significant computational resources—often thousands of GPU hours—resulting in high energy consumption and substantial carbon emissions.
This project seeks to address this gap by integrating Active Learning (AL) and Reinforcement Learning (RL) to improve the efficiency and effectiveness of optimization processes. AL will reduce training times by focusing on the most informative data, while RL will dynamically guide the optimization process. By explicitly incorporating training effort as a key evaluation criterion alongside solution quality, the project aspires to establish a more comprehensive and sustainable standard in the field.
To validate our approach, we will use the Stochastic Dial-a-Ride Problem (SDARP) as a test case. SDARP involves optimizing vehicle routing under uncertain, real-time conditions, reflecting the complexities of real-world applications such as urban transportation systems.

Position Description:
We are seeking a highly motivated postdoctoral researcher with expertise in mathematical optimization and machine learning. The successful candidate will work on designing and implementing ML-augmented optimization frameworks, with a particular focus on integrating machine learning models into decomposition techniques and developing data-efficient training strategies based on active learning. The position also involves contributing to the definition of benchmark problems and the evaluation of novel methodologies on both synthetic and real-world instances.

Main Tasks:
- Develop efficient ML models to approximate subproblems in decomposition-based algorithms
- Integrate Active Learning (AL) techniques to reduce the training dataset size
- Implement and benchmark optimization pipelines on real and synthetic instances of SDARP
- Collaborate with international experts and contribute to scientific publications

Candidate Profile:
- PhD in Operations Research, Machine Learning, Applied Mathematics, Computer Science, or a related field
- Experience with mathematical optimization and machine learning
- Knowledge of decomposition methods, active learning, or reinforcement learning is considered a plus
- Excellent coding skills (e.g., Python, Julia)
- Excellent written and oral communication skills in English

Advisory Team:
- Prof. Emiliano Traversi (ESSEC Business School, France)
- Prof. Morteza Haghir Chehreghani (Chalmers University of Technology / University of Gothenburg, Sweden)
- Prof. Ashkan Panahi (Chalmers University of Technology / University of Gothenburg, Sweden)

How to Apply:
Send your application to emiliano.traversi@essec.edu with the subject 'Postdoc Application - Learn2Opt'.

Your application should include:
- A detailed CV
- A short motivation letter (1 page)
- Names and contacts of two references
- (Optional) up to two representative publication

The position will remain open until filled.

ESSEC Business School is an equal opportunity employer and values diversity in its workforce.

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

NTU Singapore
Apply

Details

The positions pertain to the project 'Learning high-dimensional (networked) dynamical systems' (broadly defined) and will be funded by a NAP grant from NTU. 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. The duration of the PhD will typically be 4 years, while the postdoc positions will be for 2 years.

Research Fellow (Astrophysics/Data Science)

The National University of Singapore
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Details

Applications are invited for a postdoctoral research position in the Department of Physics at the National University of Singapore, in the research group of Assistant Professor Marc Hon. The successful candidate will develop and apply modern machine learning methods to the analysis of large-scale astronomical datasets, with a particular emphasis on time-domain astronomy. Research directions will be flexible and shaped according to mutual interests. Potential areas include — but are not limited to — stellar variability, exoplanets, transient phenomena, and the discovery of new astrophysical events. Projects may make extensive use of archival data from major facilities, including NASA’s Kepler, TESS, and JWST missions; ESA’s Gaia mission; and large-scale spectroscopic surveys such as LAMOST. Experience working with large, complex datasets is expected, and familiarity with modern machine learning methods will be considered an asset.

High Energy Theory Postdoc

Brookhaven National Laboratory, Upton, NY
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Details

The High Energy Theory Group (HET) has active programs in many areas of theoretical particle physics. There are strong efforts in neutrino physics, Beyond the Standard Model phenomenology, precision calculations for the LHC and intensity frontier, overlaps of particle physics and cosmology, and lattice gauge theory. The lattice efforts span weak interaction physics, precision calculations of (g-2) and PDF determinations, and the development of new algorithms, among others.

The successful candidate will be expected to conduct research focused on precision tests of the Standard Model and/or beyond the Standard Model physics at the proposed Electron-Ion collider. Priority will be given to those whose research proposal demonstrates high intellectual merit and the willingness to use machine learning and/or AI techniques.

Two-year postdoctoral positions at the Institute of Information Theory and Automation (UTIA)

The Czech Academy of Sciences, Prague, The Czech Republic
Deadline: 2025-08-29 | Apply

Details

The Institute of Information Theory and Automation (UTIA), Czech Academy of Sciences (http://www.utia.cas.cz/) invites applications for two-year postdoctoral positions in the institute beginning in January 2026.

Candidates are expected to work in one of these areas:

- artificial intelligence and machine learning,
- probabilistic graphical models,
- statistics and stochastics,
- image, video, and signal processing,
- control theory,
- adaptive decision intelligence and human-centric intelligence,
- modelling economic and financial problems,
- non-smooth analysis,
- PDEs, calculus of variations, and continuum mechanics.
- The candidates are also expected to have a strong record of, or outstanding potential for, significant research and have no more than two years since being awarded a Ph.D., Dr. or equivalent title (as of September 30, 2025). Moreover, experience in obtaining third-party funds is advantageous.
- The Institute offers a monthly salary of CZK 50 000 (about 2 000 EURO) and yearly benefits supporting e.g. recreational and sport activities, as well as health care programs. Complete applications must be received by August 29, 2025.
- In case of interest, please send your application via email to utia@utia.cas.cz. The application should include a CV, a research statement, a motivation letter, and a copy of the PhD diploma. Letter(s) of recommendation is/are welcome. They should be sent by their authors directly to the email above.

Complexity Postdoctoral Fellowship

Santa Fe Institute, NM
Deadline: 2025-10-01 | Apply

Details

The Santa Fe Institute Complexity Postdoctoral Fellowships, comprising the Omidyar Fellowships, are unique among postdoctoral appointments. The Fellowships offer early-career scholars the opportunity to undertake their own independent research within a collaborative research community that nurtures creative, transdisciplinary thought in pursuit of key insights about the complex systems that matter most for science and society. The Institute rejects compartmentalized thought common in academia. Instead, SFI scientists transcend boundaries between fields, freely synthesizing ideas spanning many disciplines – from math, physics, computer science and biology to the social sciences and the humanities – in pursuit of creative insights that advance our scientific frontiers.