PhD Position: Brain-Computer Interfaces in Cognitive Rehabilitation after Stroke

BrainStateDecoding Lab of Dr. Michael Tangermann (Computer Science Dept.); payment level: TV-L E13 (65%); duration: 3/2015-2/2017

Brain-Computer Interface (BCI) systems make use of machine learning methods to cope with the challenges which appear during the decoding of individual brain signals in real-time. Within the BrainLinks-BrainTools cluster of excellence at the University of Freiburg (Germany), we evaluate whether BCI methods can be applied clinically outside of classical control paradigms.

The PhD student will explore the use of BCI methods to support neurological rehabilitation training of speech and attention-related deficits after stroke.
Scientific challenges comprise the development and investigation of brain signal analysis methods capable to describe rapid fluctuations of brain states and to assess the dynamics of network structures connecting brain areas.

Working Areas (among others):

  • Theories (statistics, mathematics) and algorithms in the field of machine learning, with special emphasis on adaptive methods for the real-time decoding of mental states; software implementations thereof.
  • Conducting EEG studies with healthy controls and stroke patients.
  • Analysis of EEG experiments.
  • Supervision of Bachelor students.
  • Scientific dissemination of results (conferences, publications).


  • Excellent Master studies on Computer Science, Mathematics, Electrical Engineering, Biomedical Engineering, Cognitive Science or closely related fields.
  • Knowledge in theory and methods of machine learning / artificial intelligence; very good math knowledge (specifically in probability theory, statistics, linear algebra).
  • Good knowledge about the design, the analysis and implementation of algorithms; experience with mathematical software (e.g. Matlab).
  • Hands-on experience in the design, execution and analysis of electrophysiological experiments, and in signal processing of EEG-, EMG-, EOG- or ECoG data.
  • Excellent communication skills in English

The BrainStateDecoding Lab:
The international lab is embedded into the Cluster of Excellence BrainLinks-BrainTools at the University of Freiburg. The cluster provides supporting career actions for PhD students. Tight collaborations with other BLBT groups are the basis for investigating novel machine learning approaches for the real-time analysis of brain signals. The activities range from theory development to BCI applications in a clinical context and for healthy users. Implementing an equal-chances policy and family-friendly working conditions, we explicitly encourage applications of female researchers. Handicapped applicants will be given priority to non-handicapped applicants, if they have comparable qualifications. Further information:, and via Dr. Tangermann.

How to apply:
Please state the position code BCI-4 when sending your application documents (including a CV, statement of your research interests, list of publications, references, Master and Bachelor degrees and course lists) as a single PDF document via email to Dr. Michael Tangermann (This email address is being protected from spambots. You need JavaScript enabled to view it.).

Positions available at Singapore Institute for Neurotechnology

The Cognitive Engineering Laboratory (head Prof. Anastasios Bezerianos) at Singapore Institute for Neurotechnology is interested in welcoming talented and self-motivated full-time Research Fellows (RF) and Research Assistants/Associates (RA) of different levels and work experience in the exciting area of Cognitive Neuroengineering/Neuroscience and Neuroinformatics.

The Laboratory employs a multi-disciplinary approach, combining innovative neuropsychological approaches and advanced neuro-engineering techniques. Research Fellows (RF) will work on (1) multimodal brain signal processing and analysis and (2) cognitive training and brain enhancement. Research Assistants (RA) will be able to assist with research conducted in the laboratory in the above areas while developing their own expertise and skills.

Research/software engineer for BrainGate neural interface system

Our research team based at Brown University is developing brain-computer interface technology (BrainGate) to enhance communication and independence for people with tetraplegia, locked-in syndrome or limb loss. We are seeking a highly motivated individual to become a key contributor to the development of brain-computer interface technology as a research/software engineer.

Major responsibilities of the research/software engineer include the creation of novel software and integration of hardware for the BrainGate system. Software will be developed to provide clinical trial participants with direct neural control over computer-based assistive technologies, prosthetic limbs, and environmental control devices. The research/software engineer will work with other engineering staff and academic investigators to test neuroscientific hypotheses, advance the science of neural decoding and advance this brain-computer interface platform. Results will be presented at conferences or published in academic journals.

Specific responsibilities of the research/software engineer will include:

  • Develop, test and integrate software for data acquisition, real-time neural  signal processing and decoding, inter-computer communication and control of assistive devices including computers and state-of-the-art robotic arms.
  • Work closely with all team members to improve platform capability, flexibility, reliability and performance.
  • Perform frequent software integration and platform functional verification.
  • Deploy system software to clinical trial sessions and support clinical research staff to ensure successful system operation in the field.
  • Develop software and integrate hardware to integrate commercial assistive robotic devices into the BrainGate system.
  • Work with engineering and academic team to plan, enable and conduct novel research in BrainGate clinical trial sessions.
  • Conceive and implement tools for research data analysis to evaluate performance of the platform and of novel neural decoding approaches.
  • Train new team members, from students to research faculty, on procedures, methods and practices pertaining to system development and data analysis.

Relevant technical skills include: Systems engineering, digital signal processing, adaptive filtering, dimensionality reduction, classification and machine learning techniques, user interface design, analysis and presentation of complex data sets. Exceptional collaborative interpersonal skills are critical: the successful candidate will independently lead projects and provide remote technical support across clinical sites based in Boston, Providence, Cleveland, and Palo Alto.

This position requires a master's degree in computer science, neuroscience, or engineering, or equivalent experience, with proficiency in MATLAB and Simulink. Experience with Kalman filters, Bayesian statistics and programming skill in C/C++, Java, scripting languages and HTML5 confer an advantage.

Activities will be centered in Providence, RI. Travel of up to three hours from Providence may be required on an occasional basis to attend clinical research sessions.

Interested applicants should forward their CV to Drs. Leigh Hochberg and John Simeral, c/o Ms. This email address is being protected from spambots. You need JavaScript enabled to view it..

PostDoc position at Institute of Scientific Interchange

The Institute for Scientific Interchange (ISI) is seeking to appoint a highly motivated Postdoctoral Assistant to undertake research activities related to human centric computing for the Horizon 2020 project Sound Of Vision. ISI provides an unusually rich opportunity for collegial interaction in a highly competitive environment. Mentoring will also be provided by a multidisciplinary faculty team including co-investigators on the project and collaborators from Neurology, Engineering, Medicine and Psychology.

Sound of Vision (natural sense of vision through acoustics and haptics) is a highly multidisciplinary project that will design, implement and validate an original non-invasive, wearable hardware and software system to assist visually impaired people by creating and conveying an auditory representation of the surrounding environment. This representation will be created, updated and delivered to the blind users continuously and in real time. In addition to the auditory representation, haptics will be used moderately as an additional channel to convey some of the most relevant information. The system will help visually impaired people to both perceive and navigate in any kind of environment (indoor/outdoor), without the need for predefined tags/sensors located in the surroundings and regardless of the lighting conditions.

The successful applicant will:

  • conduct user and feasibility studies to determine the appropriate mobile platform and delivery components to support the functionality of the "Sound of Vision" prototype;
  • participate in the shared decision making around alternatives to the hardware and software development;
  • participate in a large trial to assist in system deployment and data collection;
  • carry out innovative, impactful research of strategic importance to the domain of behavioural neuroscience, cognitive science and human computer interaction; and
  • produce high quality scientific and technical outputs including journal articles, conference papers and presentations, patents and technical reports.

To be successful in this position you will need:

  • PhD in neuroscience, computer science, computer engineering or other related fields with a neuroscience-related background
  • demonstrated experience in behavioural neuroscience and BCI techniques. Specific areas of focus include visual impairments, brain plasticity and usability research will be desired.
  • fluency in English

The review of applications will begin immediately and the position will remain open until filled. The initial appointment is for 1 year with a possibility of extension. To apply, send a cover letter, curriculum vitae and professional reference list to the PI of the project, , This email address is being protected from spambots. You need JavaScript enabled to view it.. ISI is an equal opportunity employer and does not discriminate on the basis of race, color, national origin, gender, sexual orientation, age, religion or disability.

Post-doc/engineer position in direct brain control of muscle stimulators

The lab of Dr. Dawn Taylor currently has an opening for a postdoc or engineer for the neuroprosthetics study described below. If interested, please send a CV to This email address is being protected from spambots. You need JavaScript enabled to view it..

The long-term goal of this project is to enable paralyzed individuals to use their brain signals to control their upper limb via implanted muscle stimulators. Most labs working on brain-controlled neuroprosthetics decode intended limb kinematics (e.g. velocity, joint angles, etc.) from the recorded brain signals. However, that approach still requires converting those kinematic commands into the appropriate stimulation patterns required to generate the desired limb motion. That conversion process has not been resolved for the upper limb due to the limb's complex dynamical nature and the fact that the limb is subject to unknown external forces during use. We bypass this obstacle by retraining the brain to control muscle stimulators directly. We have come up with some novel, but clinically feasible ways of mapping neural signals directly to muscle stimulators. Our methods can enable the user to have good control over both limb motion and stiffness. To demonstrate and refine our methods, we are training monkeys to control the movements of a realistic musculoskeletal model of a paralyzed limb activated via implanted muscle stimulators. The paralyzed limb simulator (developed by the lab of Robert Krisch) provides real-time visual feedback to the animal of the limb motion that would result from stimulating the paralyzed muscles based on the animal's neural signals decoded in real time. The use of this real-time paralyzed arm simulator allows us to test and refine our process of brain-controlled muscle stimulation in monkeys without actually having to paralyze any animals.