Starting date: Positions are available immediately with a flexible starting date.
The Theoretical and Computational Neuroscience laboratory, which is part of UCLA’s world-class neuroscience community is dedicated to unraveling the principles underpinning the computations of the cerebral cortex.
The cerebral cortex is the seat of high cognitive functions. Despite the astonishing complexity of its operations, the cortex exhibits a consistent structure that likely underlies common canonical functions and computations.
Discovering these canonical computations would help us gain a deeper understanding of intelligence in the mammalian brain and shed light on neural disorders.
Our laboratory thus addresses fundamental questions that are vital to developing comprehensive theories of canonical computations in the cortex:
- How do cortical dynamics give rise to canonical computations?
- How do biophysical constraints and computational needs shape the properties of cortical circuits?
- How is information represented and transformed across populations of cortical neurons?
To tackle these questions, we employ a multidisciplinary approach that combines data-driven circuit models, biologically realistic deep learning models, abstract neural network models, artificial (deep and recurrent) neural networks, encoding and decoding models and machine learning methods to analyze the neural code.
Led by Dr. Mario Dipoppa, a computational neuroscientist with a strong experience in data-driven approaches and biological realistic modelling, the research is strongly influenced by empirical evidence from the broader neuroscience community and involves collaborations with experimental laboratories.
We are currently hiring postdoctoral researchers and research assistants. For both positions, competitive salaries and benefits will be offered.
The selected candidates will be working on questions addressing how brain computations emerge from the dynamics of the underlying neural circuits and how the neural code is shaped by computational needs and biological constraints of the brain.
We are looking for candidates, who are eager to solve fundamental questions with a creative mindset.
Candidates should have a strong publication track record in Computational Neuroscience or a related quantitative field, including but not limited to Computer Science, Machine Learning, Engineering, Bioinformatics, Physics, Mathematics, and Statistics.
Research assistant applicants
We are looking for candidates with a keen interest in gaining research experience in Computational Neuroscience, pursuing their own projects, and supporting those of other team members. Candidates should have a bachelor’s or master’s degree in a quantitative discipline and strong programming skills, ideally in Python.
UCLA is an Affirmative Action / Equal Opportunity Employer (AA/EOE). We strive to promote an inclusive and collaborative work environment. We welcome applications from diverse backgrounds and perspectives. Individuals from underrepresented groups are strongly encouraged to apply.
How to apply?
Candidates holding or about to obtain a Ph.D. degree, who are interested in joining the laboratory as postdoctoral researchers should submit:
- A CV including a publication list
- A copy of a preprint or publication in which they played a significant role (for example, first-author, co-first author, and/or corresponding author)
- A research statement describing past research and career goals (max. two pages)
- Contact information for two academic referees.
Candidates interested in joining the laboratory as research associates should send
- A CV
- A research statement describing past research and career goals (max. one page)
- Contact information for two academic referees.
The application material should be submitted as a single PDF file with your full name as the file name to email@example.com.
Informal inquiries are welcome. We strongly encourage candidates to apply early as applications will be reviewed until the positions are filled.
Details on the open positions and application instructions can be found at : www.dipoppalab.com/join-us.
Five most recent lab publications:
- Tring E, Dipoppa M, Ringach DL (2023), A power law of cortical adaptation, bioRxiv, 548134
- Di Santo S, Dipoppa M, Keller AJ, Roth MM, Scanziani M, Miller KD (2022), Unifying model for three forms of contextual modulation including feedback input from higher visual areas, bioRxiv, 493753.
- Bugeon S, Duffield J, Dipoppa M, Prankerd I, Ritoux A, Nicolotsopoulos D, Orme D, Shinn M, Peng H, Forrest H, Viduolyte A, Reddy CB, Isogai Y, Carandini M, Harris KD (2022), A transcriptomic axis predicts state modulation of cortical interneurons, Nature, 607: 330-338.
- Schmidt ERE, Zhao HT, Park JM, Dipoppa M*, Monsalve-Mercado MM*, Dahan JB, Rodgers CC, Lejeune A, Hillman EMC, Miller KD, Bruno RM, Polleux F (2021), A human-specific modifier of cortical circuit connectivity and function improves behavioral performance, Nature, 599: 640-644. *contributed equally