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Offer 197 out of 602 from 22/11/21, 11:57

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Freie Uni­ver­si­tät Ber­lin - Fachbereich Math­em­atik und Inform­atik - Insti­tut für Inform­atik - Arti­fi­cial Intel­li­gence and Machine Learn­ing Group

Our rese­arch lies in the areas of Arti­fi­cial Intel­li­gence (AI) and Machine Learn­ing (ML). Our goal is to design intel­li­gent algo­rithms that learn from data con­tinu­ously fol­low­ing the cumu­lat­ive nature of human learn­ing, while ensur­ing that what has been lear­ned is not only cor­rect but also deliv­ers pos­it­ive social impact in accord­ance with eth­ical and legal require­ments. Our cur­rent focus areas are: con­tinual learn­ing, respons­ible AI and cre­at­ive AI. This announce­ment is pub­lished sub­ject to the final appro­val by the FMER. The pro­ject will start on Dec. 1st, 2021.

Rese­arch Assist­ant (Prae­doc) (m/f/d)

Research assist­ant (prae­doc) (m/f/d)
full-time job
lim­ited to 30.11.2025
Ent­gelt­gruppe 13 TV-L FU
ref­er­ence code: ENKIS-WH-wiMi2

Working field:

AI and other com­puter sci­ence fields like data­bases (DB), data struc­tures and algorithms, net­work­ing and com­mu­nic­a­tion, secur­ity, etc., can bene­fit from each other. For example, in the case of data­bases, many tra­di­tional optim­iz­a­tion tech­niques like query optim­iz­a­tion nowadays rely on AI; on the other hand, DB tech­niques like index­ing can optim­ize AI mod­els. Like­wise, AI is used nowadays to optim­ize data struc­tures, but know­ledge of data struc­tures and algorithms is also required for AI/ML. This pos­i­tion aims at research and devel­op­ment on improv­ing the teach­ing of AI/ML sub­jects in com­puter sci­ence. Tasks include study­ing how AI is revo­lu­tion­iz­ing other CS fields but also how AI relies upon found­a­tions from these fields; integ­ra­tion into other com­puter sci­ence sub­jects like algorithms and data struc­tures, data­bases, secur­ity, etc., select­ing appro­pri­ate examples and use cases from these fields, etc. Within the frame­work of the extern­ally fun­ded research pro­ject, the oppor­tun­ity for writ­ing a doc­toral-thesis is gran­ted.

Requirements:

Applic­ants should hold an MSc or Dip­loma in Com­puter Sci­ence with a focus on Arti­fi­cial Intel­li­gence/ Machine Learn­ing/ Data Sci­ence.

Desir­able:

  • Sound under­stand­ing of Machine Learn­ing and Arti­fi­cial Intel­li­gence;
  • very good know­ledge of AI frame­works (Tensor­flow, pyt­orch, Keras, …);
  • excel­lent pro­gram­ming skills (Python, Java, …);
  • good know­ledge of core com­puter sci­ence skills (algorithms and data struc­tures, pro­gram­ming, data­bases, …);
  • teach­ing exper­i­ence (e.g., as teach­ing assist­ant) and warm interest in teach­ing and didactics of AI;
  • under­stand­ing and warm interest in AI respons­ib­il­ity aspects (bias and fair­ness, explain­ab­il­ity, …);
  • excel­lent writ­ten and verbal com­mu­nic­a­tion skills;
  • crit­ical think­ing and inde­pend­ent work;
  • will­ing­ness to learn, appre­ci­ation for team work.

How to apply:

App­lic­a­tions should be sent by e-mail, tog­e­ther with sig­ni­fic­ant doc­u­ments, indic­at­ing the ref­er­ence code, in PDF for­mat (pre­fer­ably as one doc­u­ment) no later than Decem­ber 13th, 2021 to Prof. Dr. Eirini Ntoutsi: information-ki@fu-berlin.de or pos­tal to:

Freie Uni­ver­si­tät Ber­lin
Fach­be­reich Math­em­atik und Inform­atik
Insti­tut für Inform­atik
AG Künst­liche Intel­li­genz und Maschi­nel­les Ler­nen
Mrs. Prof. Dr. Eirini Ntoutsi
Arn­im­al­lee 7
14195 Ber­lin (Dah­lem)

With an elec­tro­nic app­lic­a­tion, you ack­now­ledge that FU Ber­lin saves and pro­ces­ses your data. FU Ber­lin can­not guar­an­tee the secur­ity of your per­sonal data if you send your app­lic­a­tion over an unen­cryp­ted con­nec­tion.

Freie Uni­ver­si­tät Ber­lin is an equal oppor­tun­ity employer.