PhD position in the Lindner Lab

Field: Calcium spiking with cumulative refractoriness - statistics of the fluctuations and implications for Calcium signaling

We are looking for a PhD candidate to theoretically explore models of noisy spike-generating cellular signalling systems for which the intracellular Calcium dynamics is a prominent example. Methods will be developed within the frameworks of the theory of stochastic processes, statistical physics and nonlinear dynamics.

The project is a collaboration between the research groups of Martin Falcke (Max-Delbrueck Center for Molecular Medicine Berlin and Humboldt University Berlin) and Benjamin Lindner (Bernstein Center for Computational Neuroscience Berlin and Humboldt University Berlin) and the PhD students of both groups are expected to closely collaborate.

To be filled: Feb 1, 2019

Application due: The deadline for applications is December 31st 2018, however, later applications might also be considered.

Funding is provided for three years, starting at the latest February 1, 2019. For details on the doctoral examination process at the Physics Department of Humboldt University Berlin, see

Applications, including a letter of motivation, a CV, and a list of three potential referees should be sent by email to 

(cc to

HU is seeking to increase the proportion of women in research and teaching, and specifically encourages qualified female scholars to apply. Severely disabled applicants
with equivalent qualifications will be given preferential consideration. People with an immigration background are specifically encouraged to apply. Since we will not return
your documents, please submit copies in the application only.

The successful candidate should have a degree in physics (a background in physical biology is desirable but not obligatory), expertise in analytical calculations, programming skills (C++ or C, Python, LaTeX, Linux), and excellent command of the English language, good communication skills, and team spirit.

Benjamin Lindner
Professor for Theory of Complex Systems and Neurophysics
Bernstein Center for Computational Neuroscience Berlin
Philippstr. 13 Haus 2
10115 Berlin
phone: +49-30-2093 6336


PhD position in the Blankertz Lab

Field: Act­ive Learn­ing for bet­ter co-adapt­a­tion of user and BCIs

To be filled: as soon as possible.

Application due: Nov 2, 2018

This notice relates to the pos­i­tion in pro­ject P5 ("Act­ive Learn­ing for bet­ter co-adapt­a­tion of user and BCIs"), which focuses on research at the inter­face between com­puter sci­ence, in par­tic­u­lar machine learn­ing, and neur­os­cience. * The spe­cific tasks are: - Research in neur­o­tech­no­logy, in par­tic­u­lar in the devel­op­ment of algorithms based on machine learn­ing - Imple­ment­a­tion and val­id­a­tion of algorithms in Online BCI Sys­tems - Imple­ment­a­tion of EEG stud­ies and super­vi­sion of labor­at­or­ies - Act­ive par­ti­cip­a­tion in the DAEDALUS Research Train­ing Group - Writ­ing art­icles in sci­entific journ­als; present­a­tions at inter­na­tional con­fer­ences. - Cooper­a­tion with inter­na­tional part­ners


Suc­cess­fully com­pleted uni­versity degree (Mas­ter, Dip­lom or equi­val­ent) in Com­puter Sci­ence, Math­em­at­ics, Bio­med­ical Data Ana­lysis, Cog­nit­ive Neur­os­cience or other related dis­cip­lines; sound know­ledge of mul­tivari­ate data ana­lysis using machine learn­ing tech­niques; an excel­lent com­mand of Eng­lish, together with good aca­demic writ­ing and present­a­tion skills. Extens­ive pro­gram­ming exper­i­ence in data ana­lysis with Python or Mat­lab is desir­able. The will­ing­ness and abil­ity to work inter­dis­cip­lin­ary in an inter­na­tional team is expec­ted. Pref­er­ence is given to applic­ants with exper­i­ence in the design, imple­ment­a­tion and eval­u­ation of EEG stud­ies for decod­ing men­tal states and with expert­ise in the ana­lysis of brain sig­nals using machine learn­ing tech­niques (in par­tic­u­lar single-trial EEG clas­si­fic­a­tion).

Full information and instruction for applying:

Benjamin Blankertz
Berlin Institute of Technology
Chair for Neurotechnology
Sekr. MAR 4-3,
Marchstr. 23
D-10587 Berlin
Phone (+49 30) 314-78626


PhD position in the Obermayer Lab

Research Assistant (PhD / PostDoc position)

Field: Risk-sensitive choice and reinforcement learning under uncertainty

To be filled: April 1, 2019

Application due: Feb 28, 2019
Reference number: IV-755/18
Applications received after this date may still be considered.

Tenure: 3 years

Remuneration: E-13 TV-L, 100 %


Working field:
The successful candidate will join a DFG funded project (with Co-PI Dirk Ostwald, Freie Universität Berlin) which combines computational modelling with behavioral and fMRI experiments. The goal is to better understand human decision making and reward-based learning under perceptual uncertainty. The computational framework generalizes the risk-sensitive MDP approach of Yun et al. 2014 (Neural Comput. 26, 1298ff) to a POMDP-based behavioral modelling framework. Planned experiments address response time behavior and neural reinforcement learning processes under perceptual risk. The work program can be biased either towards computational or experimental work depending on the qualifications of the candidate.

Successfully completed university degree in a subject relevant to the work program; very good programming and English language skills; solid background in decision making and reward-based learning on a theoretical and / or experimental level; experience with computational modelling and the model-based analysis of behavioral and fMRI data is desirable.

How to apply:

Please send your application with the usual documents (CV, letter of motivation, transcripts of records, certificates, and the names of two persons who can provide recommendation letters) to Prof. Dr. Klaus Obermayer (FG Neuronale Informationsverarbeitung, Sekr. MAR 5-6, Marchstr. 23, 10587 Berlin) preferably by e-mail (

Please send copies only. Original documents will not be returned.



PhD position in the Obermayer Lab

Research Assistant (PhD position)

Field: Computational models of task dependent object-based visual attention

To be filled: October 1, 2019

Application due: Feb 15, 2019

Applications received after this date may still be considered.

Tenure: 3 years

Remuneration: E-13 TV-L, 100 %


Working field:

The successful candidate will explore the hypothesis that object-level attentional units are essential mid-level factors which guide human eye-movements in visual scene analysis. Based on eye-fixation data from visual search tasks she/he will first build computational models to emulate the measured fixation sequences, to quantify the influence of different low- and high-level visual features, and to characterize the influence of task-driven changes in object-based attention processes. In a second step, plausible models will be integrated as “attentional modules” into a computer vision system for visual scene analysis and will be evaluated in terms of task success and the number of computations involved. Potential achievement of the project is an efficient real-time analysis of dynamic visual scenes.

The position is part of the new DFG-funded cross-disciplinary research Cluster “Science of Intelligence” ( The successful candidate will be enrolled in the Cluster's doctoral program and is expected to actively engage in the Cluster's educational and research activities.

Applicants must hold a Master degree in Computational Neuroscience, Computer Science, Physics, Mathematics, or related fields. Applicants should have very good programming skills, a very good command of the English language, a solid mathematical background, competence in machine learning, and a strong interest in visual perception.

How to apply:

Please upload your application via the link and follow instructions.

Applications should include: motivation letter, curriculum vitae, transcripts of records (for both BSc and MSc), copies of degree certificates (BSc, MSc), abstracts of Bachelor-, Master-thesis, list of publications and one selected manuscript (if applicable), two names of qualified persons who are willing to provide references, and any documents you feel may help us assess your competence.