Details

Title Postdoctoral Fellow in Modular Deep Learning

School Harvard John A. Paulson School of Engineering and Applied Sciences

Department/Area Computer Science

Position Description

The Data-Centric Machine Learning lab of Prof. David Alvarez-Melis (https://dmelis.github.io/) at Harvard University, part of the ML Foundations group, has an opening for a postdoctoral position to work on novel methods for modular machine learning. The aim is to develop next-generation deep learning architectures that can be easily composed, adapted, and reused for various tasks. These architectures will be designed with principles of modularity, interpretability, and flexibility in mind, providing the foundation for scalable, robust, and efficient machine learning systems.

Responsibilities:

• Conduct innovative research on modular deep learning methods and architectures.

• Design, implement, and evaluate novel machine learning models, frameworks, and algorithms.

• Collaborate with a cross-university multidisciplinary team to integrate and apply research findings in practical scenarios, particularly in the natural sciences.

• Publish research findings in top-tier machine learning and AI conferences.

• Mentor graduate and undergraduate students in related research projects.

• Contribute to the lab’s collaborative and inclusive research culture.

  Basic Qualifications

• Ph.D. in Computer Science, Electrical Engineering, or a related field with a strong publication record in machine learning, artificial intelligence, or related areas.

• Expertise in deep learning architectures and frameworks such as TensorFlow, PyTorch and/or JAX

• Strong programming skills in Python and familiarity with scientific computing libraries.

• Demonstrated experience in one or more of the following areas: modular machine learning, transfer learning, or composable models.

• Proven ability to conduct high-quality independent research and collaborate effectively in a team environment.

• Excellent communication skills for disseminating research to both technical and non-technical audiences.

  Additional Qualifications

• Familiarity with generative modeling, Optimal Transport theory, differentiable optimization, and/or implicit deep learning

• Demonstrated interest in interdisciplinary research and applications of machine learning in fields such as physics, chemistry, biology, or healthcare.

• Prior experience mentoring students and contributing to open-source projects.

  Special Instructions

  Contact Information

Melissa Mendez

Contact Email mmendez@seas.harvard.edu

Equal Opportunity Employer

Harvard is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status.

Minimum Number of References Required 2

Maximum Number of References Allowed 3

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