Blätter-Navigation

Of­f­re 501 sur 588 du 30/06/2021, 13:55

logologo

Tech­ni­sche Uni­ver­si­tät Dres­den - “Fried­rich List“ Faculty of Trans­port and Traf­fic Sci­en­ces, Insti­tute of Trans­port and Eco­no­mics, Chair of Big Data Ana­ly­tics in Trans­por­ta­tion

The TU Dres­den is one of ele­ven Ger­man uni­ver­si­ties that were iden­ti­fied as an “excel­lence uni­ver­sity”. TUD has about 36.500 stu­dents and almost 5319 employees, 507 pro­fes­sors among them, and, thus, is the lar­gest uni­ver­sity in Sax­ony, today.

Having been com­mit­ted to sci­en­ces and the engi­nee­ring before the reuni­fi­ca­tion of Ger­many, TU Dres­den now is a multi-disci­pline uni­ver­sity, also offe­ring huma­nities and social sci­en­ces as well as medi­cine.

Rese­arch Asso­ciate / PhD Stu­dent

(sub­ject to per­so­nal qua­li­fi­ca­tion employees are remu­ne­ra­ted accord­ing to salary group E 13 TV-L)
The posi­tion is star­ting Octo­ber 1, 2021. The posi­tion is limi­ted for 3 years with the option of exten­sion. The period of employ­ment is gover­ned by the Fixed Term Rese­arch Con­tracts Act (Wis­sen­schafts­zeit­ver­trags­ge­setz - WissZeitVG). The posi­tion aims at obtai­ning fur­ther aca­de­mic qua­li­fi­ca­tion (e.g. PhD).

Work­ing field:

  • tea­ching sup­port (in Ger­man and Eng­lish);
  • exami­na­tion sup­port;
  • super­vi­sion of semi­nar papers and the­ses;
  • admi­nis­tra­tive tasks;
  • rese­arch on the topics of the chair;
  • sci­en­ti­fic publi­ca­ti­ons;
  • pre­sen­ta­tion of rese­arch results at rele­vant con­fe­ren­ces.

Require­ments:

  • very good uni­versity degree in Data Sci­ence, Com­puter Sci­ence, Stat­ist­ics, Trans­port­a­tion Eco­nom­ics (focus: Data Ana­lyt­ics, Eco­no­met­rics or Optim­iz­a­tion), Inform­a­tion Sys­tems, Math­em­at­ics or a com­par­able course of study. In addi­tion to sub­stan­tial skills in data ana­lyt­ics, we expect know­ledge of the rel­ev­ant pro­gram­ming lan­guages (espe­cially R and/or Python) and in-depth know­ledge in at least one of the fol­low­ing top­ics:
  • (Large-Scale) Data Sci­ence (includ­ing Data Ana­lyt­ics, Stream Min­ing, Deep Learn­ing, Visu­al­iz­a­tion);
  • Machine Learn­ing (e.g., AutoML, Algorithm Selec­tion / Con­fig­ur­a­tion, Fea­ture Selec­tion, Hyper­para­meter Optim­iz­a­tion, Uncer­tainty Quan­ti­fic­a­tion, Robust­ness, Fed­er­ated Learn­ing);
  • Explain­able AI / Inter­pretable Machine Learn­ing (e.g., com­pre­hens­ib­il­ity of algorithmic beha­vior, social impact of algorithms);
  • Optim­iz­a­tion (e.g., evol­u­tion­ary, multi-object­ive, mixed-integer, large-scale);
  • Bench­mark­ing (e.g., per­form­ance eval­u­ation, prob­lem char­ac­ter­iz­a­tion);
  • Route plan­ning (e.g., vehicle rout­ing, trav­el­ing sales­per­son prob­lem);
  • Time series ana­lysis.
We request very good Eng­lish skills as well as a strong pas­sion for answer­ing to and devel­op­ing solu­tions for data ana­lyt­ical ques­tions. We are look­ing for a pro­act­ive and highly com­mit­ted team-player with a high degree of inde­pend­ence. Ger­man lan­guage skills and some ini­tial exper­i­ence in answer­ing ques­tions related to trans­port­a­tion and/or eco­nom­ics are desir­able. The same applies to uni­versity teach­ing (for example, by super­vising tutori­als), sci­entific work and/or the imple­ment­a­tion of col­lab­or­at­ive soft­ware pro­jects (includ­ing the use of Git, Docker, etc.).

How to ap­ply:

Applic­a­tions from women are par­tic­u­larly wel­come. The same applies to people with dis­ab­il­it­ies.
Please sub­mit your com­pre­hens­ive applic­a­tion by July 30, 2021 (stamped arrival date of the uni­versity cent­ral mail ser­vice applies) by mail to:

TU Dresden
Fak­ultät Verkehr­swis­senschaften „Friedrich List“
Insti­tut für Wirtschaft und Verkehr
Pro­fes­sur für Big Data Ana­lyt­ics in Trans­port­a­tion
Prof. Dr. Pas­cal Ker­schke
Helm­holtz­str. 10
01069 Dresden

or via the TU Dresden Secure­Mail Portal https://securemail.tu-dresden.de by send­ing it as a single pdf doc­u­ment to

pascal.kerschke@tu-dresden.de.

Please sub­mit cop­ies only, as your applic­a­tion will not be returned to you. Expenses incurred in attend­ing inter­views can­not be reim­bursed.


Ref­er­ence to data pro­tec­tion: Your data pro­tec­tion rights, the pur­pose for which your data will be pro­cessed, as well as fur­ther inform­a­tion about data pro­tec­tion is avail­able to you on the web­site: https://tu-dresden.de/karriere/datenschutzhinweis