Tenure-Track Assistant Professor – Computational Neuroscience

Laplace Expert Ltd; Austin (TX); On-site; USD 90,000 - 130,000; Full time

Job summary

University of Texas at Austin invites applications for a Tenure-Track Assistant Professor in Computational Neuroscience. The successful candidate will develop independent research integrating computational/theoretical neuroscience with machine learning and AI, collaborating across brain science disciplines in Austin.

The position includes research, teaching, and scholarly collaboration, with access to the Texas Advanced Computing Center and multidisciplinary networks.

Qualifications

• PhD or equivalent with training in computer science, neuroscience or a related field.

Responsibilities

• Develop an independent research program in computational or theoretical neuroscience.

Skills

Computational neuroscience

Machine learning

Theoretical neuroscience

Education

PhD or equivalent in CS/Neuroscience

Job description

Tenure-Track Assistant Professor – Computational Neuroscience

University of Texas at Austin

United States

22 November 2026 (full-consideration date, not established as an absolute cutoff)

About This Opportunity

University of Texas at Austin advertises Tenure-Track Assistant Professor – Computational Neuroscience, an academic opportunity focusing on Computational and theoretical neuroscience connecting machine learning, AI and brain science in Austin.

The Department of Neuroscience at the University of Texas at Austin is recruiting a tenure-track computational neuroscience faculty member. The successful scholar will develop an independent research programme using computational or theoretical methods to connect neuroscience with artificial intelligence, machine learning and related quantitative sciences. Current faculty research spans cellular and molecular neuroscience, behaviour, cognition and systems neuroscience, with potential intersections in brain–computer interfaces, computational psychiatry and precision medicine. The position includes research, university teaching and scholarly collaboration, with access to the Texas Advanced Computing Center and multidisciplinary neuroscience and computing networks. The institutional call expects a research plan and evidence of training and mentoring activities.Who This Opportunity Is Best Suited For:Candidates whose professional or academic interests connect closely to Computational and theoretical neuroscience connecting machine learning, AI and brain science are the principal thematic audience. Relevant qualifications and documented teaching, research or leadership experience must be assessed against the official recruitment criteria rather than inferred from the title. Documented qualifications and preparation include: PhD or equivalent with training in computer science, neuroscience or a related field.Potential Challenges / Who May Find It Less Suitable:Applicants should assess the exact selection requirements and funding/contract terms before committing to this specific application.Overall Opportunity Assessment:This opportunity is distinguished by its focus on Computational and theoretical neuroscience connecting machine learning, AI and brain science. Its documented methods and collaborations provide a concrete basis for assessing methodological fit; application and financial details should be confirmed against the institution’s vacancy or programme instructions.

Computational and theoretical neuroscience connecting machine learning, AI and brain science Neuroscience

Level Researcher

Field Computational and theoretical neuroscience connecting machine learning, AI and brain science

Organization Type University

Funding & Compensation

Amount Paid

Funding Type Salary

Funding Status Paid

Requirements

Education

PhD or equivalent with training in computer science, neuroscience or a related field.

Experience

Technical Skills Computational neuroscience Machine learning Theoretical neuroscience