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Max-Planck-Insti­tut für Bil­dungs­for­schung - Depart­ment: Humans and Machi­nes

The Max Planck Insti­tute for Human Deve­lop­ment is dedi­ca­ted to the study of human deve­lop­ment and edu­ca­tion. Rese­ar­chers of various disci­pli­nes – inclu­ding psy­cho­logy, edu­ca­tion, socio­logy and medi­cine, as well as history, eco­no­mics, com­pu­ter sci­ence and mathe­ma­tics – work tog­e­ther on inter­di­sci­pli­nary pro­jects at the Ber­lin Insti­tute. The rese­arch ques­ti­ons they examine include how people make effec­tive decisi­ons even under time pres­sure and infor­ma­tion over­load, how the inter­ac­tion bet­ween beha­viour and brain func­tion chan­ges over the life­span, as well as how human emo­ti­ons change in a his­to­ri­cal con­text and how they have affec­ted the course of history its­elf.

Research Assist­ant

Work­ing field:

The Centre for Humans and Machines (CHM) within the Max Planck Insti­tute for Human
Devel­op­ment is seek­ing a Research Assist­ant to work on. The selec­ted stu­dent will work under Dr. Nic­colo Pes­cetelli and CHM Dir­ector Assoc. Prof. (MIT) Iyad Rah­wan, Ph.D.

The researcher will be required to per­form data ana­lysis and
numer­ical sim­u­la­tion for a pro­ject involving the imple­ment­a­tion of
optim­iz­a­tion algorithms.

The researcher will be respons­ible for the fol­low­ing tasks:
  • Imple­ment­ing sev­eral known optim­iz­a­tion mod­els
  • Design and imple­ment a pipeline for sim­u­la­tion test­ing and data col­lec­tion
  • Per­form robust­ness checks using vari­ous data sources
  • Doc­u­ment find­ings, meth­ods and code in an appro­pri­ate format for sub­mis­sion to a gen­eral sci­ence journal.
  • Assist with the sub­mis­sion pro­cess includ­ing revi­sions and responses to edit­ors and review­ers.
  • Other pos­sible tasks that arise dur­ing the course of the research.

The pro­ject has an ini­tial dur­a­tion of 6 months with pos­sib­il­ity to
renew, depend­ing on per­form­ance and pro­ject-spe­cific needs.

Require­ments:

Cur­rently enrolled in Com­puter sci­ence stud­ies (prefer­ably mas­ter stu­dents)
  • Gradu­ate level know­ledge of optim­iz­a­tion tools and algorithms
  • Know­ledge of Python 3.5+
  • Know­ledge of at least one machine learn­ing or optim­iz­a­tion
lib­rary
  • Rel­ev­ant exper­i­ence in aca­demia or industry

How to ap­ply:

Please send your applic­a­tion without a photo, as one single PDF-doc­u­ment to
pescetelli@mpib-berlin.mpg.de. The applic­a­tion should include (a) a short motiv­a­tion
let­ter, (b) CV, and (c) rel­ev­ant school/uni­versity cer­ti­fic­ates and ref­er­ences.

The Max Planck Soci­ety strives for gender and diversity equal­ity. We wel­come applic­a­tions from all back­grounds. The Max Planck Soci­ety is com­mit­ted to increas­ing the num­ber of indi­vidu­als with dis­ab­il­it­ies in its work­force and there­fore encour­ages applic­a­tions from such qual­i­fied indi­vidu­als.

The data pro­tec­tion declar­a­tion for the pro­cessing of per­sonal data
in the con­text of your applic­a­tion can be found here:
https://www.mpib-berlin.mpg.de/860935/de_infos_bewerbung.pdf