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Tech­ni­sche Uni­ver­si­tät Dres­den - Faculty of Elec­tri­cal and Com­pu­ter Engi­nee­ring, Insti­tute of Com­mu­ni­ca­ti­ons Tech­no­logy, Deut­sche Tele­kom Chair of Com­mu­ni­ca­tion Net­works

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 (m/f/x)

(sub­ject to per­so­nal qua­li­fi­ca­tion employees are remu­ne­ra­ted accord­ing to salary group E 13 TV-L)

The pro­ject posi­tion is star­ting as soon as pos­si­ble. The posi­tion is initi­ally limi­ted till March 31, 2023. The period of employ­ment is gover­ned by § 2 (2) Fixed Term Rese­arch Con­tracts Act (Wis­sen­schafts­zeit­ver­trags­ge­setz – WissZeitVG).

Working field:

We are cur­r­ently loo­king for a rese­arch assi­stant to sup­port our Machine Lear­ning team for a rese­arch pro­ject. The team is cur­r­ently working on rese­ar­ching sca­ling of cloud sys­tems. The approach cur­r­ently being worked on comes from the field of load pre­dic­tion with uncer­tainty. The task is to help imple­ment, test and docu­ment the exis­ting approach in a sui­ta­ble test­bed. The con­cep­tion is still adap­ta­ble, should chan­ges be recom­men­ded. If necessary, adjust­ments and opti­mi­za­ti­ons to the approach may still be app­lied. After suc­cess­ful imple­men­ta­tion, a cor­re­spon­ding eva­lua­tion will take place. We will publish the results of our work in sui­ta­ble (inter­na­tio­nal) sci­en­ti­fic publi­ca­ti­ons.

Requirements:

uni­ver­sity degree (Diploma/Mas­ter) in com­pu­ter sci­ence, com­mu­ni­ca­ti­ons engi­nee­ring or com­mu­ni­ca­ti­ons tech­no­logy; pro­gramming skills, pre­fer­a­bly Python (JAX, numpy, pan­das); expe­ri­ence in using con­tai­ner solu­ti­ons (docker, lxc, etc.) as well as orches­tra­tion (swarm, Kuber­ne­tes, k3s, pre­fer­a­bly hosted); inte­rest in Machine Lear­ning, espe­ci­ally Gaus­sian Pro­ces­ses; good know­ledge of Eng­lish and good oral and writ­ten com­mu­ni­ca­tion skills. Know­ledge of theo­re­ti­cal com­mu­ni­ca­ti­ons engi­nee­ring and in data ana­ly­sis and sto­chastic pro­ces­ses are also highly recom­men­ded.

How to apply:

App­li­ca­ti­ons from women are par­ti­cu­larly wel­come. The same app­lies to people with disa­bi­li­ties.
Please send your app­li­ca­tion docu­ments until June 28, 2022 (stam­ped arri­val date of the uni­ver­sity cen­tral mail ser­vice app­lies) pre­fer­a­bly via the TU Dres­den Secu­re­Mail Por­tal https://securemail.tu-dresden.de by sen­ding it as a sin­gle pdf docu­ment to karin.domel@tu-dresden.de or to: TU Dres­den, Fakul­tät Elek­tro­tech­nik und Infor­ma­ti­ons­tech­nik, Insti­tut für Nach­rich­ten­tech­nik, Deut­sche Tele­kom Pro­fes­sur für Kom­mu­ni­ka­ti­ons­netze, z. Hd. Frau Karin Domel, Helm­holtz­str. 10, 01069 Dres­den. 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.