DRESDEN-concept Career Offers

Task: The suc­cess­ful can­did­ates will be work­ing on imple­ment­ing a sim­u­la­tion model of a radio net­work and on radio resource alloc­a­tion using machine learn­ing algorithms in the con­text of the pro­ject DAKORE, fun­ded by the Ger­man Fed­eral Min­istry of Edu­ca­tion and Research. Under the assump­tion of advanced power amp­li­fi­ers, that provide high energy effi­ciency for a range of oper­at­ing points (the power amp­li­fi­ers will be developed by another chair at TUD), a dis­trib­uted arti­fi­cial intel­li­gence can adapt­ively con­trol the net­work to reduce the energy con­sump­tion while main­tain­ing a high qual­ity of ser­vice for all users. This includes the adap­tion of para­met­ers of a set of access points (e.g., trans­mis­sion power) and the alloc­a­tion of users to these access points (dif­fer­ent access points may use dif­fer­ent fre­quency ranges). The envi­sioned dynamic con­trol of a dis­trib­uted net­work can, for example, be tackled using multi-agent rein­force­ment learn­ing. In order to ensure seam­less com­pli­ance with the reques­ted qual­ity of ser­vice of all users, a pre­dict­ive engine may also be part of the over­all sys­tem. The pro­ject leaves much space for cre­ativ­ity and the imple­ment­a­tion of the can­did­ates’ interests. Suc­cess­ful can­did­ates will be required to per­form the fol­low­ing tasks:

• Carry out research in the emer­ging topic arti­fi­cial intel­li­gence for radio resource alloc­a­tion (both devel­op­ing a net­work sim­u­la­tion and an intel­li­gent net­work man­age­ment sys­tem).
• Col­lab­or­ate with col­leagues at TUD and with industry part­ners.
• Dis­sem­in­ate res­ults through sci­entific pub­lic­a­tions in the top-tier ven­ues.
• Present res­ults in top-tier inter­na­tional con­fer­ences and work­shops.
• The pos­i­tion may also include minor teach­ing duties and/or con­tri­bu­tions in the devel­op­ment of new research pro­pos­als.

• The can­did­ate should pos­sess a uni­versity degree in elec­trical engin­eer­ing, tele­com­mu­nic­a­tion engin­eer­ing, com­puter sci­ence, or equi­val­ent.
• The ideal can­did­ate should have know­ledge and exper­i­ence in sev­eral of the fol­low­ing top­ics:

o wire­less com­mu­nic­a­tions, in par­tic­u­lar in the con­text of 5G,
o wire­less net­work sim­u­la­tion,
o machine learn­ing, in par­tic­u­lar multi-agent rein­force­ment learn­ing,
o radio resource alloc­a­tion/ Radio resource man­age­ment (e.g., band­width and power alloc­a­tion, schedul­ing, etc.).
o An under­stand­ing of power amp­li­fi­ers and high-fre­quency hard­ware is bene­fi­cial towards under­stand­ing the lar­ger pic­ture of the pro­ject but not required.

• Good pro­gram­ming skills are required. Highly rel­ev­ant pro­gram­ming lan­guages are Python, C++, and MAT­LAB.
• Flu­ent writ­ten and verbal com­mu­nic­a­tion skills in Eng­lish are required.

Applic­a­tions from women are par­tic­u­larly wel­come. The same applies to people with dis­ab­il­it­ies.

Please sub­mit your applic­a­tion doc­u­ments until August 17, 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.