Connectionists: Post-doctoral Fellowship -- Automatic Detection of Epileptic Seizures Using Brain Activity Data

[Rosana Veroneze]

*Post-doctoral Fellowship -- Automatic Detection of Epileptic Seizures
Using Brain Activity Data*


Post-doctoral grand funded by FAPESP (current grand values in
http://www.fapesp.br/3162)

Field of knowledge: Computer Engineering; Machine Learning

FAPESP process # 2013/07559-3

Project title: BRAINN - The Brazilian Institute of Neuroscience and
Neurotechnology

Working topics: Supervised Learning; Feature Extraction; Pattern
Classification

Number of places: 1

Starting date: March 15th 2019

Principal investigator: Fernando J. Von Zuben

Institution: School of Electrical and Computer Engineering (UNICAMP) –
Campinas, SP – Brazil

Data of announcement: February 1st 2019

Deadline for submissions: February 22nd 2019

Address: Av. Albert Einstein, 400 – Campus Zeferino Vaz, Campinas – SP,
Brazil

E-mail for proposal submission: vonzuben at dca.fee.unicamp.br

Please, use as e-mail subject: [POSTDOC – BRAINN: Detection of Epileptic
Seizures]


*Summary*


The Laboratory of Bioinformatics and Bioinspired Computing (LBiC) of the
School of Electrical and Computer Engineering - Unicamp is looking for a
highly qualified candidate for a post-doctoral fellowship to work on the
automatic detection of epileptic seizures using data analytics and machine
learning techniques. The fellowship is granted by the Sao Paulo Research
Foundation -- FAPESP for a period of 24 months, with exclusive dedication
(40 hours per week). *The proposal submission deadline is February 22**th*
* 2019.*


*Job Requirements and Primary Responsibilities*


The selected candidate will work on the project entitled "BRAINN - The
Brazilian Institute of Neuroscience and Neurotechnology", more specifically
in the subproject “Multi-view multi-task learning methods to improve
detection of epileptic seizures in multiple-patient datasets characterized
by rare seizure events”. This project aims at investigating the impact of
methodologies with multiple tasks (multiple patients) and multiple views
(multiple feature extractions) to improve the quality of detection of
epileptic seizures dealing with cases of scarcity or absence of occurrences
of epileptic seizure events in the recorded data.


Primary responsibilities include but are not limited to:

   -

   Development of machine learning methodologies capable of improving the
   performance in the detection of epileptic seizures, aiming at becoming the
   state-of-the-art in the field when dealing with scarce data, multiple
   patients and multiple feature sets.
   -

   Development and possible improvement of methodologies of feature
   extraction in EEG data, including the use of techniques of representation
   learning, usually associated with deep architectures in artificial neural
   networks.
   -

   Involvement in task-forces devoted to managing, integrating, analyzing,
   and interpreting brain activity datasets, thus converting raw data into a
   more informative representation capable of helping decision making.
   -

   Validation of the proposed solutions in clinical conditions.
   -

   Production of high-impact technical and scientific papers, patents and
   prototypes with socioeconomic potential value.
   -

   Active participation in the training of human resources in the research
   area.
   -

   Composition of reports and documentations associated with the project.


*Skills and experience*



   -

   Ph.D. in Electrical Engineering, Computer Engineering, Computer Science,
   Data Science, Statistics, or Applied Mathematics;
   -

   Experience in advanced solutions for machine learning is required.
   -

   Previous contact with medical datasets (mainly EEG), multitask learning,
   datasets with unbalanced data and multi-view learning is desirable but not
   mandatory.
   -

   Testified ability to perform statistical or mathematical formulation and
   proficiency in solving optimization problems is required.
   -

   Complete understanding of at least one computer language widely used in
   machine learning (e.g. Python, C, Matlab or R) is mandatory.
   -

   Proficiency in statistical or mathematical formulation environments
   (e.g. Matlab, R, SciPy, Gurobi, Sklearn and CVXOPT) is desirable but not
   mandatory.


*Personal skills*



   -

   Strong motivation to work independently and as part of a
   multidisciplinary team.
   -

   Good skills in communication and in the establishment of partnerships
   with other research groups with common interests, including those composing
   CEPID / BRAINN.
   -

   Proactive and leadership spirit.


*Languages*


Communication and writing skills in English are required;


*How to apply*


Submissions and questions should be directed to Fernando J. Von Zuben’s
e-mail: [vonzuben at dca.fee.unicamp.br]. Foreign applicants are welcome to
apply.

Please, use [POSTDOC – BRAINN: Detection of Epileptic Seizures] as the
subject of the e-mail and attach:

(1) Curriculum vitae.

(2) Names of two references with contact information.

(3) A two-page summary of past research and relevant qualifications.

(4) A link to a personal Web page, if available, where further information
can be found.
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