PhD position (all genders) in Computational Neuroscience and NeuroAI

Universitätsklinikum Hamburg Eppendorf; Hamburg; On-site; EUR 64,000 - 91,000; Full time

Benefits offered by this job

HPC access

Conference attendance

Career development

Job summary

Universitätsklinikum Hamburg Eppendorf invites applications for a PhD project in computational neuroscience and AI. The successful candidate will work on biologically inspired connectivity principles to influence recurrent neural networks and real-world learning tasks.

Collaboration spans computational, experimental, and clinical researchers within the institute. The role emphasizes independent scientific work, development of novel AI models, and potential for scholarly output and funding

Qualifications

• Master’s degree or equivalent qualification in a relevant field such as computational neuroscience, computer science, physics, mathematics, biomedical engineering or related quantitative discipline.
• Fluent written and spoken English for scientific discussions and writing.
• Independent scientific thinking and productivity.

Responsibilities

• Contribute to development of bio-inspired RNN and reservoir computing models.
• Explore modular and core-periphery architectures for scalable learning tasks.
• Translate biological wiring principles into artificial neural network designs and low-precision weights.

Skills

Python

Recurrent neural networks

Reservoir computing

State-space models

Education

Master's degree in computational neuroscience or related quantitative field

Equivalent qualification by start date

Job description

Over the past decades, neuroscience has made remarkable progress in characterizing the architecture of biological neural networks. We now possess increasingly detailed descriptions of synaptic connectivity, cellular composition, laminar organization, mesoscale topology, and large-scale connectomes across multiple species. Across these diverse levels of organization, common structural principles have begun to emerge, including modularity, core-periphery organization, asymmetric reciprocity, and multi-scale network organization. Yet, despite this wealth of anatomical knowledge, a fundamental question remains unresolved: how do these structural features shape neural computation? Which aspects of biological connectivity are essential for flexible, robust, and adaptive information processing, and which are simply byproducts of development or evolution?

This PhD project aims to bridge this gap by combining computational and network neuroscience with artificial intelligence to investigate how biologically inspired connectivity principles influence computation in recurrent neural networks.

Research Environment

The Institute of Computational Neuroscience is a small, active and internationally diverse group working at the interface of computational neuroscience, network science, neuro-inspired artificial intelligence and clinical neuroscience.

The successful candidate will be jointly supervised by Prof. Claus C. Hilgetag and Dr. Fatemeh Hadaeghi.

About The Project

A central methodological framework will be recurrent neural networks and reservoir computing models. Our goal is to uncover how biological wiring and functional organization can inform artificial neural network design and to translate these insights into models that operate effectively with low-precision (binary or ternary) weights.

Main Aims Are To

• Explore bio-inspired backbones for unimodal temporal learning
• Develop modular and core-periphery architectures for multimodal learning
• Explore Scalability and real-world application.

The work will involve close interaction with a strong computational and network neuroscience team at our institute. The position provides substantial scope for scientific initiative and intellectual ownership. The successful candidate may also contribute to the development of related research proposals and future funding applications.

The position is funded through third-party research funding, comprises 65% of the regular weekly working hours, and provides the opportunity for obtaining the academic qualification of a PhD.

Your Profile

• Master’s degree or equivalent qualification by the starting date in a relevant field such as computational neuroscience, computer science, physics, mathematics, biomedical engineering or a related quantitative discipline
• Independent scientific thinking and productivity
• The ability and interest to understand complex mathematical or computational concepts
• Fluent written and spoken English, enabling full participation in scientific discussions, presentations and academic writing
• Prior experience with recurrent neural networks (RNNs), including reservoir computing or state space models, is advantageous
• Strong analytical and computational skills, including proficiency in Python;
• Prior practical experience in areas directly related to the project is advantageous
• Scientific initiative, quantitative aptitude and willingness to learn (Evidence of scientific independence may include an ambitious Master’s project, publications or preprints, conference contributions, contributions to scientific software, open-source work or other substantial independent research activity.)

Description

In addition, please provide a brief description, no more than one page, of one research or technical project in which you played a substantial independent role. Please explain:

• what scientific or technical question you addressed
• what your own contribution was
• what difficulties you encountered and how you dealt with them
• what the project ultimately produced or taught you

This description is intended to help us understand how you approach scientific problems and how much intellectual ownership you have taken in previous work.

Immunity status

Please note that employment is contingent upon proof of immunization or immunity against the measles virus, in accordance with applicable legal and medical requirements. Documentation (e.g., vaccination certificate) must be provided before employment begins.

Our Offer

• Close interaction with computational, experimental and clinical collaborators
• Access to high-performance computing infrastructure
• Opportunities to attend conferences, workshops and summer schools
• Substantial scientific independence within a collaborative research environment
• Fair and transparent compensation in accordance with our collective bargaining agreement (TVöD/VKA) (€ 63,600 – € 91,100), taking into account qualifications and professional experience. The salary range applies to a full-time position of 38.5 hours per week. The amounts listed are guidelines and do not constitute a salary guarantee.
• Targeted and individualized professional development at both the technical and project levels is provided, offering long-term prospects in a meaningful work environment
• Comprehensive continuing education and training programs at our UKE Academy for Education and Career
• Opportunities to help shape our “UKE INside” personnel policy through cross-functional and cross-hierarchical projects
• Sustainable commuting: Subsidies for the Deutschlandticket as a job ticket and Dr. Bike bicycle service
• Excellent health, prevention, and sports programs
• Family-friendly work environment: Partnerships for childcare, free vacation care, counseling for employees with family members requiring care
• A secure job, meaningful work, and a supportive team environment
• Structured onboarding and open knowledge sharing within the team
• Our employee restaurant offers a wide variety of culinary options; additional options are available at the “Health Kitchen” cafés and bistros and at a supermarket located directly on the premises

Contact

Contact to the department

Dr. Fatemeh Hadaeghi

[phone]

[e-mail]

[phone]

About Us

We live diversity and value variety

We offer a work environment that provides equal opportunities regardless of age, gender, sexual identity, disability, ethnic and social origin, or religion. This is confirmed by our accession to the Charter of Diversity. We explicitly aim to increase the proportion of women in management positions, especially among scientific personnel in research and teaching. Women with equal qualifications will be given priority. The same applies in the case of under-representation of one gender in the advertised area. Persons with severe disabilities with equal aptitude, competence, and professional performance will be given priority.