Research Intern - Multimodal Deep Learning for Healthcare - Microsoft Resea at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

03 Mar, 26

Salary

0.0

Posted On

03 Dec, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Signal Processing, Medical Imaging, Computational Biology, Healthcare Data, Representation Learning, Self-Supervised Learning, Unimodal Learning, Multimodal Learning, Interpretability Methods, Transformers, Biomedical Imaging, Bioinformatics, Natural Language Processing, Causal Machine Learning

Industry

Software Development

Description

Research Interns put inquiry and theory into practice. Alongside fellow doctoral candidates and some of the world's best researchers, Research Interns learn, collaborate, and network for life. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides. During the 12-week internship, Research Interns are paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer. We are looking for candidates who have intellectual curiosity and passion to solve real-world problems in healthcare using machine learning. Responsibilities will include: Co-development of an internship project in collaboration with the supervisor Design, implementation and evaluation of new machine learning methods and models Presentation and communication of research findings Currently enrolled in a PhD program in areas such as computer science (e.g. machine learning, deep learning, signal processing), medical imaging, computational biology, medicine. In addition to the qualifications below, you'll need to submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter. Prior experience with deep learning frameworks (e.g., PyTorch) and some familiarity with software engineering practices (e.g. git). Passion for healthcare and medicine. Experience with healthcare data. Ability to work and learn in a collaborative and diverse environment. Representation learning, self-supervised learning, unimodal or multimodal learning. Interpretability methods for deep learning (e.g. mechanistic interpretability, intrinsically interpretable methods, representation engineering, circuit discovery or rule extraction). Design, training, or evaluation of large unimodal or multimodal transformers. Biomedical imaging such as radiology, computational histopathology. Computational biology including -omics, bioinformatics, when coupled with deep learning. Clinical data integration or multimodal fusion. Large language models for healthcare and medicine, biomedical natural language processing, post-training of LLMs/RLAIF. AI for scientific discovery, including hypothesis generation, biomarker discovery. Causal machine learning. Track record of publication in conferences or journals within machine learning and/or healthcare.

Responsibilities

Research Interns will co-develop an internship project in collaboration with their supervisor and are expected to design, implement, and evaluate new machine learning methods and models. They will also present their findings and contribute to the research community.