Tenure-Track Assistant Professor - DS/AI for Science & Engineering

Boston University - Faculty of Computing & Data Sciences; Boston (MA); On-site; USD 110,000 - 160,000; Full time

Job summary

Boston University invites applications for a tenure-track assistant professor position in the Faculty of Computing & Data Sciences. We invite candidates pursuing foundational or use-inspired Data Science or AI research with interdisciplinary impact.

The successful candidate will teach at undergraduate and graduate levels, develop a vigorous research program, and contribute to BU-wide collaboration and service.

Qualifications

• PhD in computing or a discipline relevant to foundational or use-inspired computing research.
• Strong record of research and evidence of scholarly impact.
• Demonstrated capacity for collaboration and commitment to excellence in teaching at undergraduate and graduate levels.

Responsibilities

• Teach undergraduate and graduate courses in computing and data sciences.
• Develop and lead an active, externally funded research program.
• Mentor and supervise students and postdocs.
• Collaborate with colleagues across BU and the CDS to advance interdisciplinary AI/DS initiatives.

Education

PhD in computing or related field

Job description

Boston University (BU) invites applications for a tenure-track assistant professor position in the Faculty of Computing & Data Sciences (CDS).

All candidates pursuing foundational or use-inspired Data Science (DS) or Artificial Intelligence (AI) research will be considered. Candidates working in one or more of the following broad areas of AI for Science research are especially encouraged to apply.

• DS/AI-driven Biological, Biomedical, and Life Sciences, including structural biology, macromolecular design, genomic foundation models, computational neuroscience, predictive epidemiology, and AI-accelerated drug and therapeutics discovery.
  
• DS/AI-driven Natural, Physical, and Earth Sciences, including physics-informed machine learning, climate and environmental modeling, physics and astrophysics applications, and digital twinning for material design and manufacturing.
  
• DS/AI for science methodologies, frameworks, and platforms, including neuro-symbolic AI, causal inference, world models, federated learning, multi-agent reinforcement learning, and AI platforms for explainable and reproducible experimental science.

The University expects excellence in teaching and in research, qualifications required of all applicants include a PhD in a discipline relevant to foundational or use-inspired computing research; a strong record of research; a demonstrated capacity for collaboration; and a commitment to innovation in teaching at the undergraduate and graduate levels.