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.
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
Post-Baccalaureate Program at Kempner Institute for the Study of Natural and Artificial Intelligence
Harvard University, Cambridge, Massachusetts
Apply
Details
Applications are open for the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University's Post-Baccalaureate Program.Offered in partnership with the Harvard Griffin Graduate School of Arts and Sciences (GSAS) Office for Equity, Diversity, Inclusion and Belonging, the Kempner post-baccalaureate program is a fully funded two-year training program designed to prepare recent college graduates for PhD programs in intelligence research, including fields like computer science, machine learning, neurobiology, and cognitive science.
Academic Opportunities
Faculty Opportunities
Assistant Professor - Computer Science-Information Technology Program
James Madison University, Harrisonburg, Virginia
Apply
Details
The Department of Computer Science at James Madison University invites applications for two tenure-track or renewable term appointment (RTA) positions at the rank of Assistant Professor, to serve in the new Information Technology Major within the Department of Computer Science, to begin August 2025. More information about the department is available at http://www.jmu.edu/cs/ and more information about the Information Technology (IT) major can be found at http://www.jmu.edu/it/.Associate Professor/Professor in Machine Learning
UiT (The Arctic University of Norway), Tromsø, Norway
Deadline: 2025-01-27 | Apply
Details
The Department of Physics and Technology has up to three open permanent positions as Associate Professor and/or Professor. Early career scientists, with a promising CV, as well as experienced candidates are invited to apply, respectively for Associate and full Professor.The faculty members will join the UiT Machine Learning Group. The group is internationally recognized, with research ranging from foundational machine learning methodology and algorithms to applied AI development. The range of applications is wide, with a particular focus on healthcare. The new faculty members shall further strengthen the scientific excellence and high-profile of the group, notably in the Centre of Research-based Innovation SFI Visual Intelligence, which is headed by the group, and the Centre of Excellence SFF Integreat – The Norwegian Centre for Knowledge-based Machine Learning.
The workplace is at UiT in Tromsø. You must be able to start in the position in Tromsø within 6 months after receiving the offer.
Postdoc Opportunities
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.Open Postdoctoral Positions in Machine learning, Signal Processing, and Image Analysis
Centre for Computational and Data Science, University of Oslo, Norway
Apply
Details
We offer opportunities to work in a highly dynamic group environment where you will interact with our team of renowned scientists in machine learning, image analysis, and signal processing. DSTrain project applications with us can cover fundamental methodological aspects of machine learning and computational science, as well as domain-oriented, data-rich studies focused on upstream tasks within our group’s established and emerging application areas, such as:1. Visual Intelligence: Beyond applying Machine Learning to Images
2. Medical ultrasound imaging
3. Understanding memory and emotion with Machine Learning
4. Machine Learning in Real World (Theory, Methods, and Applications)
5. Graph Neural Networks for Hierarchical Structured Learning
Open Postdoctoral Research Position
Max Planck Institute & Saarland University, Saarbruecken, Germany
Deadline: 2025-1-31 | Apply
Details
Most code is now written as a collaboration between humans and code-assistive technologies, which are based on foundation models, trained on text and source code. How can we ensure that this collaboration is most effective? In this ambitious project, we will develop methods based on machine learning, cognitive science, and software engineering that improve the effectiveness of human-AI collaboration in the domain of code generation. An example of a concrete research question of interest is how to adapt the model output based on inferences about the user's cognitive state. We will also validate our developed methods via large-scale crowdsourced human experiments as well as in-person studies with a more focused population of programmers.Ideal candidate:
We are looking for a Postdoctoral Researcher to lead this project. Suitable candidates will have strong computational backgrounds, and have research experience with 1) large language models for text and/or code generation and 2) running human user studies. Experience with analyzing and modeling brain imaging data (EEG and/or fMRI) is beneficial, but not required. Fluent communication in written and spoken English is required.
Team:
We are a team of computer scientists and cognitive scientists who are working closely together. Specifically, the faculty and groups involved in this project are:
-Sven Apel, Chair of Software Engineering, Saarland University
-Adish Singla, Machine Teaching Group, Max Planck Institute for Software Systems
-Mariya Toneva, Bridging AI and Neuroscience Group, Max Planck Institute for Software Systems
The candidate will have the opportunity to extensively interact with all groups, collaborate on exciting relevant projects (e.g. see our recent joint preprint for an example), and mentor PhD, masters, and bachelors students. The position is in Saarbruecken, Germany. The official language of this collaboration is English.
How to apply:
Please send the following to the PIs (apel@cs.uni-saarland.de, adishs@mpi-sws.org, mtoneva@mpi-sws.org) by Jan 31, 2025 AoE:
-Updated CV
-List of your 2-3 most relevant publications or preprints
-A brief research statement on what kind of related research directions you would like to work on (1-2 pages)
-A brief statement on why you're interested in this position (1-2 paragraphs)
-Contact information of 2 references. References will be requested at a later date, only once an initial screening is completed.
-Preferred start date and duration
Postdocs and PhD students in Machine Learning
Finnish Center for Artificial Intelligence (FCAI) and ELLIS Unit, Helsinki, Finland
Deadline: 2025-2-2 | Apply
Details
Finnish Center for Artificial Intelligence FCAI and ELLIS Unit Helsinki invite applications for research positions in machine learning. You will join one of the top AI research centers in the Nordics and in Europe, with access to an excellent network of scientists and a broad range of possibilities to work with companies.Postdoctoral researcher positions in Probabilistic Machine Learning research group, Aalto University
Aalto University, Helsinki, Finland
Deadline: 2025-2-2 | Apply
Details
Samuel Kaski’s research group Probabilistic Machine Learning is searching for postdocs to work on AI fundamentals in exciting projects. The work includes collaboration with the Finnish Center for Artificial Intelligence (FCAI), the Centre for AI Fundamentals at the University of Manchester, the Alan Turing Institute, ELLIS and the new ELLIS Institute Finland, and researchers from other fields.Postdoctoral Fellowships in Deep Learning
NYU Shanghai, Shanghai, China
Deadline: 2025-1-31 | Apply
Details
NYU Leonard Stern School of Business and NYU Shanghai are excited to announce an open call for Postdoctoral Fellowship applications. The position may start in Spring or Fall 2025 and will be under the supervision of Profs. Bruno Abrahao (NYU Shanghai) and João Sedoc (NYU Stern).Other Academic Opportunities
A3D3 Postbaccalaureate Fellow
University of Washington, A3D3, Seattle, Washington
Deadline: 2025-02-16 | Apply
Details
The Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute funded by the National Science Foundation (NSF), under the Harnessing the Data Revolution (HDR) program, is seeking postbaccalaureate research fellows to join our interdisciplinary teams of scientists and engineers to develop and deploy artificial intelligence (AI) to accelerate science discoveries in particle physics, astrophysics, biology, and neuroscience. We strongly encourage women and individuals from traditionally underrepresented groups in STEM, such as African American/Black, Chicano/Latino, Native American/Alaska Native, Native Hawaiin/Pacific Islander, and Filipino, to apply.Industry Opportunities
Machine Learning Researcher
Secondmind Labs, Cambridge, England
Apply
Details
This is an exciting opportunity to join a team at the forefront of artificial intelligence and machine learning. Secondmind Labs consists of researchers and engineers that explore innovative ideas that can improve the state of the art in Probabilistic modelling and Bayesian optimization and tackle real-world challenging problems.Summer 2025 Internship Opportunities
Toyota Research Institute, Multiple Locations
Apply