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Bun­des­an­stalt für Mate­ri­al­for­schung und -prü­fung - Sec­tion S.3 "eSci­ence“ in Ber­lin-Ste­glitz

The Bun­des­an­stalt für Mater­i­alfor­schung und -prü­fung (BAM) is a mater­i­als rese­arch organ­iz­a­tion in Ger­many. Our mis­sion is to ensure safety in tech­no­logy and chem­istry. We per­form rese­arch and test­ing in mater­i­als sci­ence, mater­i­als engin­eer­ing and chem­istry to improve the safety of pro­ducts and pro­ces­ses. At BAM we do rese­arch that mat­ters. Our work cov­ers a broad array of top­ics in the focus areas of energy, infra­struc­ture, envir­on­ment, mater­i­als, and ana­lyt­ical sci­ences.

Under­gradu­ate Assist­ant (m/f/d) in the field of study Com­puter Sci­ence, Stat­ist­ics, Machine Learn­ing or com­par­able

35 - 40 hours/month
Tem­por­ary con­tract for 6 month
Hourly wage 12,68 Euro

Work­ing field:

The eSci­ence team devel­ops stat­ist­ical and machine learn­ing meth­ods in col­lab­or­a­tion with other depart­ments at BAM. In this pro­ject, you will develop meth­ods for the auto­mated pro­cessing and ana­lysis of small-angle X-ray scat­ter­ing data (SAXS). The five-meter-long "Meth­od­o­logy Optim­iz­a­tion for Ultrafine Struc­ture Explor­a­tion"-instru­ment, or MOUSE for short, can quantify nano­struc­tures over an unpre­ced­en­ted wide range from 0.2 to 2000 nano­met­ers in large sample volumes. New data cor­rec­tion and eval­u­ation meth­ods developed at BAM com­plete the instru­ment and are essen­tial to reach­ing its full poten­tial. Machine learn­ing meth­ods need to be developed to address the cur­rent meas­ure­ment meth­od­o­logy bot­tle­neck: allow­ing for auto­mated clas­si­fic­a­tion of large num­bers of exper­i­mental meas­ure­ments. Suc­cess in this pro­ject will lead to adop­tion of the ML meth­ods to other tech­niques as well.

  • Pre­par­a­tion of data sets using data aug­ment­a­tion tech­niques or sim­u­la­tions
  • Devel­op­ment and test­ing of machine learn­ing mod­els to clas­sify and tag meas­ure­ments
  • Integ­ra­tion of developed meth­ods into exist­ing pipelines
  • Doc­u­ment­a­tion of source code
  • Sup­port­ing fur­ther tasks in the field of data sci­ence


  • Stu­dent of com­puter sci­ence, machine learn­ing, or com­par­able sub­ject, prefer­ably with a bach­elor's degree
  • Exper­i­ence with machine learn­ing meth­ods and image ana­lysis
  • Very good know­ledge of rel­ev­ant pro­gram­ming lan­guages includ­ing Python (and prefer­ably its machine learn­ing lib­rar­ies)
  • Exper­i­ence with con­trib­ut­ing to col­lab­or­at­ive soft­ware pro­jects using ver­sion con­trol sys­tems (e.g., Git)
  • Interest in phys­ical sci­ences using spe­cial­ized, one-off equip­ment
  • Good know­ledge of Eng­lish
  • Abil­ity to work, organ­ize and struc­ture tasks inde­pend­ently
  • Will­ing­ness to occa­sion­ally attend inter­con­tin­ental vir­tual meet­ings in the even­ing

You are enrolled at a Ger­man uni­versity for the period of employ­ment.
The max­imum work­ing time with a part-time job is 80 hours per month.

What we of­fer:

  • Inter­dis­cip­lin­ary rese­arch at the inter­face of polit­ics, eco­nom­ics and soci­ety
  • Work in natio­nal and inter­na­tio­nal net­works with uni­ver­sit­ies, rese­arch insti­tutes and indus­trial com­pan­ies
  • Out­stand­ing facil­it­ies and infra­struc­ture
  • Flex­ible work­ing hours, mobile work­ing

How to ap­ply:

We wel­come applic­a­tions via the online applic­a­tion form

until 11. April 2021. Altern­at­ively, you can also send your applic­a­tion by post, quot­ing the ref­er­ence num­ber 76/21-S.3 to:

Bundes­an­stalt für Mater­i­alforschung und -prü­fung
Referat Z.3 - Per­sonal
Unter den Eichen 87
12205 Ber­lin

Dr Ben­ner will be glad to answer any spe­cific ques­tions you may have. Please get in touch via the tele­phone num­ber +49 30 8104-3647 and/or by email to

BAM pur­sues the goal of pro­fes­sional equal­ity between women and men. We there­fore par­tic­u­larly wel­come applic­a­tions from women. In addi­tion, BAM sup­ports the integ­ra­tion of severely dis­abled per­sons and there­fore expressly wel­comes their applic­a­tions. With regard to the ful­fil­ment of the job advert­ise­ment require­ments, the applic­a­tion doc­u­ments are examined indi­vidu­ally. Recog­nised severely dis­abled per­sons will be given pref­er­en­tial con­sid­er­a­tion if they are equally suit­able.

The advert­ised pos­i­tion requires a low level of phys­ical aptitude.