We are looking for highly motivated coworkers to join our inter­di­sci­plinary lab “Machine Biopho­tonics” in the Rudolf Virchow Center at the University of Würzburg in Germany. Our lab develops compu­ta­tional and experi­mental methods for trans­la­tional biology, with a focus on imaging-based antibiotic drug discovery. Ongoing and/or planned projects include:

• AI for image-based pheno­typic screening
• Generative AI for drug design
• Machine learning for metabo­lomics
• In silico drug screening
• AI for micro­bio­lo­gical diagnosis
• Compu­ta­tional imaging for pheno­typic drug screening
• Explainable AI for biome­dicine
• CRISPRi screens, spatial transcrip­tomics for pheno­typic screening

For more infor­mation about our main research interests, please visit our webpage.

Five postdoc positions are open in the Machine Biopho­tonics Lab at University of Würzburg (Germany), and two positions in our sister lab, the Imaging and Modeling Unit at Institut Pasteur (Paris, France).

The positions are funded in part by the Rudolf Virchow Center, the Bavarian High Tech Agenda, the EU ERC Synergy project “AI4AMR” (colla­bo­ration with I. Boneca, Inserm/Institut Pasteur and M. Brönstrup, Helmholtz Centre for Infection Research) and the Agence Nationale de la Recherche (ANR).

Appli­cants should hold (or be close to obtaining) a PhD in one of the following or related fields:

• Compu­ta­tional biology, bioin­for­matics, chemo­in­for­matics
• Physics, biophysics, engineering
• AI, computer science, applied mathe­matics
• Drug discovery, high content screening
• Micro­biology, cell biology, molecular biology

We are seeking highly motivated candi­dates with a strong interest in inter­di­sci­plinary research, an excellent work ethic, strong teamwork skills, and fluency in English (both spoken and written).

To apply, please send us a single PDF containing:

• A cover letter explaining your motivation to join us and how your expertise aligns with our research
• A detailed CV including a publi­cation list and a summary of your past research achie­ve­ments (5 pages max)
• Contact details of at least three referees, preferably former or current super­visors
• Copies of transcripts and diplomas

Die Univer­si­täts­me­dizin Göttingen (UMG) verfolgt im Rahmen ihrer strate­gi­schen Planung die
konse­quente Weiter­ent­wicklung ihrer profil­bil­denden Forschungs­schwer­punkte Molekulare Zellbio-
logie, Neuro­wis­sen­schaften, Herz-Kreislauf-Medizin und Onkologie mit trans­la­tio­nalen Ansätzen u.a.
als Partner­standort der Gesund­heits­for­schungs­zentren Deutsches Zentrum für Herz-Kreislauf-For-
schung (DZHK), Deutsches Zentrum für Neuro­de­ge­nerative Erkran­kungen (DZNE) und Deutsches
Zentrum für Kinder- und Jugend­ge­sundheit (DZKJ). Die UMG ist auf dem Göttingen Campus eng
vernetzt mit den natur- und biowis­sen­schaft­lichen Einrich­tungen der Univer­sität sowie den
außer­uni­ver­si­tären Einrich­tungen am Standort.

An der UMG ist eine
Junior­pro­fessur
Medizi­nische Infor­matik — Klinische Entschei­dungs­un­ter­stützung
(Bes. Gr. W1 tenure track W2)

zum nächst­mög­lichen Zeitpunkt im Institut für Medizi­nische Infor­matik (Leitung: Prof. Dr. Dagmar
Krefting) zu besetzen. Die Ernennung erfolgt zunächst für die Dauer von drei Jahren. Bei positiver
Zwischen­eva­luation wird das Dienst­ver­hältnis um weitere drei Jahre verlängert. Die Überleitung in eine
Lebens­zeit­pro­fessur (W2) ohne Ausschreibung erfolgt nach einer positiven Evaluation.
Die Tenure Track-Position bietet eine verläss­liche Karrie­re­per­spektive und vielfältige Koope­ra­­tions-
und Vernet­zungs­mög­lich­keiten in der Fakultät und auf dem Göttingen Campus. Sie soll perspek­ti­visch
mit der wissen­schaft­lichen Leitung eines Bereichs im Institut für Medizi­nische Infor­matik verbunden
sein.

Die Bewer­be­rinnen und Bewerber sollten erste Forschungs­er­fah­rungen mit der Unter­stützung
klini­scher Entschei­dungs­pro­zesse durch statis­tische oder infor­ma­tische Methoden nachweisen und
unter­schied­liche klinische Entschei­dungs­si­tua­tionen kennen gelernt haben. Ihnen sind die neuen
Heraus­for­de­rungen in indivi­dua­li­sierten Medizin im Hinblick auf seltene Erkran­kungen und
Langzeit­ver­läufe bekannt.

Die Ausschreibung richtet sich an Bewer­be­rinnen und Bewerber in einer frühen Phase ihrer
wissen­schaft­lichen Karriere, die nach einer heraus­ra­genden Promotion dabei sind, ein eigen­stän­diges
wissen­schaft­liches Profil zu entwi­ckeln. Erfah­rungen in der Lehre, der kompe­ti­tiven Einwerbung von
Dritt­mitteln und eine inter­na­tionale Vernetzung sind erwünscht.
Die Einstel­lungs­vor­aus­set­zungen für Junior­pro­fes­so­rinnen und Junior­pro­fes­soren ergeben sich aus §
30 des Nieder­säch­si­schen Hochschul­ge­setzes in der zurzeit geltenden Fassung. Die UMG besitzt das
Berufungs­recht.
Bewer­bungen von Wissen­schaft­le­rinnen und Wissen­schaftlern aus dem Ausland sind ausdrücklich
erwünscht.
Teilzeit­be­schäf­tigung kann unter Umständen ermög­licht werden.

Die UMG strebt eine Erhöhung des Frauen­an­teils an und fordert daher quali­fi­zierte Frauen aus-
drücklich zur Bewerbung auf. Schwer­be­hin­derte werden bei entspre­chender Eignung bevorzugt
berück­sichtigt.
Bitte reichen Sie Ihre Bewer­bungs­un­ter­lagen webba­siert unter https://berufungsportal.umg.eu
bis spätestens 26.10.2025 (4 Wochen) ein.
Bei Fragen stehen wir unter berufungsportal@med.uni‐goettingen.de gerne zur Verfügung.

THE GROUP: The candidate will be part of the Compu­ta­tional Systems Biology group at the University of Salzburg, which is headed by Nikolaus Fortelny (https://plus.ac.at/fortelny). The group is focused on integrating single-cell and spatial multi-omics data with biolo­gical knowledge using AI/ML and other compu­ta­tional approaches in cancer and immunology research. We focus on approaches that produce robust, reliable, and ideally inter­pr­e­table results.

THE PROJECT: Cancer treatment is difficult because patients respond differ­ently to therapies and because therapy responses are challenging to predict beforehand. The candidate will develop compu­ta­tional models to predict therapy response in breast cancer based on images (H&E stains) alone or in combi­nation with spatial omics data. The candidate will closely colla­borate with partners from the hospital of Salzburg and the group of Fritz Aberger from the university, who have a biobank of samples and will generate the data. The project is funded by the state of Salzburg in the presti­gious “AI-call” that is focused on bringing AI into real-world appli­ca­tions.

THE PLACE: The city of Salzburg has ample natural and cultural attrac­tions as well as fast connec­tions to Vienna and Munich. It is surrounded by beautiful lakes and mountains, with various oppor­tu­nities for recrea­tional and sports activities. The university has 18 000 students, and a highly colla­bo­rative research environment in both biome­dical and compu­ta­tional sciences.

QUALI­FI­CA­TIONS:
— PhD in bioin­for­matics, biotech­nology, computer science, statistics, physics, or similar
— Excellent scien­tific thinking and commu­ni­cation, high level of motivation and drive
— Programming skills (python) and AI/ML frame­works (pytorch, tensorflow or similar)
— Experience with predictive models, ideally for image analysis, evidenced by publi­ca­tions
— An under­standing of and interest in basic biome­dical concepts
— Excellent English and commu­ni­cation skills

DETAILS:
— Start date: ideally January 2025
— Duration: 3 years
— Salary and work hours according to Austrian funding regula­tions
— Place of work: Salzburg, Austria

OUR OFFER:
— Research in bringing AI/ML and spatial omics into the clinic
— Cutting-edge academic environment (https://scholar.google.at/citations?user=IHjaqgkAAAAJ)
— Being part of an inter­na­tional, inter­di­sci­plinary, and fun team
— Oppor­tu­nities for self-growth through courses for hard and soft skills
— Parti­ci­pation in confe­rences and project meetings
— Excellent social benefits of working in Austria (holidays, health insurance, …)
— Environment that values a healthy work / life balance

Enthu­si­astic scien­tists, who are motivated to develop AI/ML models for real-world biome­dical appli­ca­tions are encou­raged to apply. Please send a letter describing your motivation (one page), CV, and names of 2–3 reference contacts to nikolaus.fortelny@plus.ac.at, writing “Appli­cation Postdoc Digital Pathology 2025” in the subject line.

DEADLINE: October 26th, 2025.

We look forward to hearing from you!

Die Univer­si­täts­me­dizin Göttingen (UMG) verfolgt im Rahmen ihrer strate­gi­schen Planung die konse­quente Weiter­ent­wicklung ihrer profil­bil­denden Forschungs­schwer­punkte Molekulare Zellbio­logie, Neuro­wis­sen­schaften, Herz-Kreislauf-Medizin und Onkologie mit trans­la­tio­nalen Ansätzen u.a. als Partner­standort der Gesund­heits­for­schungs­zentren Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Deutsches Zentrum für Neuro­de­ge­nerative Erkran­kungen (DZNE) und Deutsches Zentrum für Kinder- und Jugend­ge­sundheit (DZKJ). Die UMG ist auf dem Göttingen Campus eng vernetzt mit den natur- und biowis­sen­schaft­lichen Einrich­tungen der Univer­sität sowie den außer­uni­ver­si­tären Einrich­tungen am Standort.

Das Institut für Arbeits‑, Sozial- und Präven­tiv­me­dizin der Univer­si­täts­me­dizin Göttingen mit seinen Stand­orten Göttingen und Wolfsburg (https://arbeitsmedizin.umg.eu) erforscht moderne Arbeits­welten in einer digita­li­sierten Gesell­schaft. Es legt einen beson­deren Fokus auf gesell­schaft­liche, indus­trielle und unter­neh­me­rische Trans­for­ma­ti­ons­pro­zesse, Digita­li­sierung sowie den Einsatz Künst­licher Intel­ligenz (KI) zur Förderung von Prävention und Gesundheit im Arbeits­kontext. Das seit Ende 2024 unter neuer Leitung stehende Institut für Arbeits‑, Sozial- und Präven­tiv­me­dizin der UMG gehört zum Bereich der Versor­gungs­for­schung der UMG mit einer Spezia­li­sierung auf KI unter­stützte Forschungs­an­sätze. Für die Weiter­ent­wicklung dieses Profils ist eine

Univer­si­täts­pro­fessur

KI in der Arbeits‑, Sozial- und Präven­tiv­me­dizin (Bes. Gr. W2)

zum nächst­mög­lichen Zeitpunkt auf Dauer zu besetzen.

Das Institut ist eng vernetzt mit weiteren wissen­schaft­lichen Einrich­tungen und Fakul­täten der Univer­sität Göttingen sowie mit überre­gio­nalen Initia­tiven wie dem Campus-Institut Data Science (CIDAS), der Sektion Medizi­nische Daten­wis­sen­schaften (MeDaS), und dem Nieder­säch­si­schen Zentrum für KI und kausale Methoden in der Medizin (CAIMed). Koope­ra­tionen mit inter­na­tio­nalen Großun­ter­nehmen, darunter die Volks­wagen AG, ermög­lichen Zugang zu umfang­reichen und vielfäl­tigen Daten­sätzen für die Forschung.

Die Stellen­in­ha­berin oder der Stellen­in­haber (m/w/d) soll den Bereich der Künst­lichen Intel­ligenz im Kontext von Arbeits‑, Sozial- und Präven­tiv­me­dizin in Forschung und Lehre maßgeblich vertreten und gemeinsam mit der Insti­tuts­leitung weiter­ent­wi­ckeln. Die Integration von KI in die Arbeits- und Sozial­me­dizin steht noch am Anfang, ihre aktive Gestaltung ist eine zentrale Zukunfts­aufgabe für arbeits- und sozial­me­di­zi­nische Forschung, Praxis und Lehre.

Ihre Aufgaben:

• Aufbau des Forschungs­schwer­punktes „KI in der Arbeits‑, Sozial- und Präven­tiv­me­dizin“
  • Einwerben dritt­mit­tel­ge­för­derter Forschungs­pro­jekte
  • Engagement in der univer­si­tären Lehre und Betreuung von Dies umfasst bestehende Lehrauf­gaben der Univer­si­täts­me­dizin Göttingen und der Univer­sität Göttingen sowie der Konzep­tua­li­sierung neuen Lehran­gebote mit den Schwer­punkt KI.
  • Aufgaben der akade­mi­schen Selbst­ver­waltung

• Grund­la­gen­for­schung in der KI und kausalen Methoden wie die Entwicklung, Unter­su­chung und Anwendung von Methoden zur kausalen Inferenz und zur Evaluation von Künst­licher Intel­ligenz
• (Weiter-)Entwicklung prädik­tiver Systeme im Kontext Gesundheit, Visua­li­sierung komplexer Zusam­men­hänge, Bild- und Video­ana­lysen sowie Muster­er­ken­nungen
• Entwicklung, Validierung und Imple­men­tierung von KI-Algorithmen und ‑Tools zur Analyse und Vorhersage gesund­heits­be­zo­gener Ereig­nisse, Optimierung von Prozessen sowie zur Entschei­dungs­un­ter­stützung im
• Validierung von Modellen zur Sicher­stellung der Genau­igkeit und Zuver­läs­sigkeit
• Auswertung umfang­reicher Daten (Big Data) (z. B. von Kranken­kassen und Großun­ter­nehmen) im Kontext von Gesundheit und Prävention

Anfor­de­rungen: Profil

• einschlä­giges abgeschlos­senes wissen­schaft­liches Hochschul­studium (z. B. Statistik, Infor­matik, KI, Epide­mio­logie, Mathe­matik, Data Science oder einem verwandten Bereich)
• Heraus­ra­gende Promotion in Statistik, Mathe­matik, KI oder einem verwandten Fach mit starken quanti­ta­tiven Kompo­nenten
• eigen­ständige wissen­schaft­liche Tätigkeit auf dem Gebiet der KI, Statistik oder Data Science nach der Promotion
• Erfahrung in  der  Entwicklung  und  Anwendung  von  Methoden  zur  kausalen  Inferenz  und Evaluation von Künst­licher Intel­ligenz, präfe­riert in der Medizin
• Kennt­nisse in Daten­bank­tech­no­logien und Big Data
• Erfah­rungen bei       der       Einwerbung          von       Dritt­mitteln          und       in       inter­dis­zi­pli­nären Forschungs­ko­ope­ra­tionen
• Erfah­rungen in der Lehre und akade­mi­schen Selbst­ver­waltung
• Sprach­kennt­nisse (CEFR-Niveau): Englisch mindestens C1
• Kennt­nisse im Bereich Gesund­heits­daten und ‑systeme sind von Vorteil

Die Einstel­lungs­vor­aus­set­zungen ergeben sich aus § 25 des Nieder­säch­si­schen Hochschul­ge­setzes in der zurzeit geltenden Fassung. Die UMG besitzt das Berufungs­recht.

Bewer­bungen  von  Wissen­schaft­le­rinnen  und  Wissen­schaftlern  aus  dem  Ausland  sind  ausdrücklich erwünscht.

Die UMG strebt eine Erhöhung des Frauen­an­teils an und fordert daher quali­fi­zierte Frauen ausdrücklich zur Bewerbung auf. Schwer­be­hin­derte werden bei entspre­chender Eignung bevorzugt berück­sichtigt.

Bitte  reichen  Sie  Ihre  Bewer­bungs­un­ter­lagen  webba­siert  unter  https://berufungsportal.umg.eu  bis spätestens 12.11.2025 ein.

Bei Fragen stehen wir unter berufungsportal@med.uni‐goettingen.de gerne zur Verfügung.

The Faculty of Biology and Psychology at the University of Göttingen invites appli­ca­tions for a temporary profes­sorship with civil servant status (grade W2 NBesO) with tenure track (grade W3 NBesO) at the Institute of Micro­biology and Genetics and the Campus Institute Data Science (CIDAS) starting at the earliest possible date. The profes­sorship is part of the programme for digiti­sation profes­sor­ships for Lower Saxony (“Digita­li­sie­rungs­pro­fes­suren für Nieder­sachsen”) by the Lower Saxony Ministry of Science and Culture (MWK):

W2 Profes­sorship

with tenure track to W3 in

Compu­ta­tional Biology (f/m/d)

The successful candidate will initially be appointed for a period of five years. Transfer to a permanent profes­sorship (W3) shall take place after a positive evaluation without the position being readver­tised.

We are looking for a committed and team-oriented colleague to represent the field of data-driven research and teaching in life sciences. The post holder will perform research and teaching in the field of big data methods. This may include their explo­ration, develo­pment and appli­cation, e.g., digiti­sation, analysis, visua­li­sation, and integration of large amounts of data obtained using high-throughput methods on biomole­cules (genomics, transcrip­tomics, proteomics, metabo­lomics, etc.) and/or imaging methods/ high-throughput methods of pheno­typing (phenomics). In addition to identi­fying colla­bo­ration oppor­tu­nities with ongoing initia­tives, developing concepts for new colla­bo­rative projects in the field of biology is of crucial importance.

In terms of teaching, the post holder will contribute to inter­di­sci­plinary Bachelor’s and Master’s degree programmes by repre­senting the field of biolo­gical data analysis. Streng­thening and further develo­pment of the Bachelor’s degree programmes “Biology” and “Applied Data Science”, as well as of the Master’s degree programmes “Applied Data Science” and “Compu­ta­tional Biology and Bioin­for­matics” will be the focus. Furthermore, parti­ci­pation in the Bachelor’s degree programme “Bioche­mistry” will be expected. The holder of the position will be further expected to contribute to the expansion of data expertise in the Master’s degree programmes “Molecular Life Science” and “Develo­p­mental, Neuronal and Behavioral Biology”. The University of Göttingen is committed to research-oriented teaching.

Appointment requi­re­ments are stipu­lated by Article 25 of the Lower Saxony Higher Education Act in the most current version of that law. As a rule, junior professors and other members of the University of Göttingen can only be considered for a profes­sorial appointment if after the completion of the doctoral thesis they have trans­ferred to another university or worked for a minimum of two years in research elsewhere. The University of Göttingen is a Public Law Foundation and thereby entitled to award profes­sor­ships. Details will be provided upon request.

Appli­ca­tions from abroad are expli­citly welcome. The University of Göttingen strives to increase the proportion of women in areas where women are under­re­pre­sented and therefore expli­citly invites qualified female scholars to apply. The University has committed itself to being a family-friendly insti­tution and supports its employees in balancing work and family life. The University is parti­cu­larly committed to the profes­sional parti­ci­pation of disabled employees and therefore welcomes appli­ca­tions from persons with disabi­lities. In the case of equivalent quali­fi­ca­tions, appli­ca­tions from disabled persons will be given prefe­rence. In order for the University to be able to protect the interests of the applicant, infor­mation about a disability or equal status should be included in the appli­cation.

Please send your appli­cation as one PDF file including CV, a publi­cation list, a list of your acquired third-party funds, and a record of your teaching and research activity to the Dean’s Office of the Faculty of Biology and Psychology (dekanbio@uni-goettingen.de) by October 23, 2025.

If you have further questions, please contact Prof. Dr. Rolf Daniel (dekanbio@uni-goettingen.de) or Prof. Dr. Jan de Vries (devries.jan@uni-goettingen.de).

The submission of the appli­cation consti­tutes consent under the data protection law and allows us to process your appli­cation data. For more details on the legal basis and data use, please refer to the infor­mation sheet on the General Data Protection Regulation (GDPR).

Please note that only the German version of this job announcement is legally binding (https://uni-goettingen.de/de/700631.html).

We seek a highly motivated Compu­ta­tional Scientist (m/f/d) for Cancer Antigen Discovery to join our Compu­ta­tional Genomics unit. We are an inter­di­sci­plinary team of scien­tists, PhD students, and software engineers passionate about developing bioin­for­matics tools to identify biomarkers and thera­peutic targets for perso­na­lized immuno­the­rapies against cancer. In close colla­bo­ration with other teams at TRON, as well as with partners from academia, clinics and industry, we apply our compu­ta­tional approaches to discover antigen targets and translate them into clinical practice.

The successful candidate will be respon­sible for identi­fying and priori­tizing novel classes of tumor-specific antigens for perso­na­lized mRNA vaccines and other immuno­the­rapies. This will be achieved through the analysis of multi-omics data, using innovative compu­ta­tional approaches supported by validation through molecular and immuno­lo­gical assays.

Your tasks and respon­si­bi­lities:

• Analyze large cohorts of genomic, epige­nomic, transcrip­tomic and proteomics data to identify and prioritize novel tumor-specific candidate antigens relevant for cancer immuno­the­rapies.
• Develop compu­ta­tional pipelines for antigen target detection in individual patients for perso­na­lized therapies.
• Contribute to the design of validation strategies and colla­borate on confirming targets through molecular and immuno­lo­gical assays.
• Present and discuss in internal meetings and inter­na­tional confe­rences, writing R&D reports and scien­tific publi­ca­tions.

What you bring:

• D. in Compu­ta­tional Biology, Immuno-Oncology, or a related data-driven field with at least 2 years of postdoc­toral research experience in academia or industry.
• Demons­trated scien­tific expertise in tumor immunology and target discovery for immuno­the­rapies.
• Excellent programming skills for repro­du­cible data analysis in Python or R.
• Experience analyzing next-generation sequencing (NGS) data. Experience with mass spectro­­­metry-based immun­o­pep­ti­domics data analysis is a plus.
• Hands-on expertise with version control systems (e.g., Git), workflow managers (e.g., Nextflow or Snakemake), and high-perfor­­­mance computing environ­ments.
• Experience leading research projects in a multi­di­sci­plinary environment and comfor­table working in a dynamic and evolving environment.

Enthu­siasm and curiosity for the diverse activities of our research institute completes your profile.

We offer:

• A dynamic, innovative, and creative research environment with strong expertise in immuno­the­rapies.
• An open, collegial, and supportive working atmosphere in a respectful organiza­tional culture
• A highly diverse and inclusive workforce
• Access to our GPU-accele­rated HPC cluster and labora­tories with cutting-edge sequencing techno­logies and molecular assays.
• Perfor­­­mance-based remune­ration and other benefits
• Oppor­tu­nities for perso­na­lized profes­sional develo­pment
• Conve­nient access via public transport and car as well as bicycle parking spaces
• The possi­bility of hybrid working arran­ge­ments

TRON is an inter­na­tio­nally recog­nised institute for appli­­­­­cation-oriented research. We combine the strengths of academic research with the requi­re­ments of quality-controlled indus­trial develo­p­ments. At TRON, we share a common mission to develop innovative solutions for the immuno­the­ra­peutic treatment of cancer, infec­tious diseases and other serious diseases with high medicinal need for develo­pment.

TRON was founded in Mainz in 2010 and works in close coope­ration with univer­sities and hospitals as well as with regional, national and inter­na­tional research insti­tu­tions and pharmaceu­tical companies.

As part of our team, you have the oppor­tunity to be at the forefront of trans­la­tional science with us.

If all this appeals to you, we look forward to getting to know you.

Please send us your complete and infor­mative appli­cation documents (cover letter, CV, references) in a single document of max. 5 MB by e‑mail to Human Resources at jobs (at) tron-mainz.de,
Job-ID: 43104–25–02-WAPRO.

For more infor­mation, visit our homepage at www.tron-mainz.de

We are seeking a motivated Bioin­for­matics Software Developer (m/f/d) to join our Compu­ta­tional Genomics unit. We are an inter­di­sci­plinary team of scien­tists, PhD students, and software engineers who develop bioin­for­matics tools, predictive models, and data analysis pipelines to identify biomarkers and thera­peutic targets for perso­na­lized immuno­the­rapies against cancer. In close colla­bo­ration with multiple teams at TRON, as well as with partners from academia, clinics, and industry, we apply and validate our compu­ta­tional approaches and translate them into clinical practice.

The successful candidate will develop and maintain bioin­for­matics software and fully repro­du­cible, end-to-end workflows to analyze diverse biolo­gical datasets, including genomics and transcrip­tomics sequencing data from large cohorts of tumor samples.

Your tasks and respon­si­bi­lities:

• Design, implement, and maintain compu­ta­tional analysis tools and pipelines for high-throughput sequencing data, including WGS, WES, RNA-seq, scRNA-seq, spatial transcrip­tomics and long-read sequencing.
• Improve in-house bioin­for­matics pipelines to enhance accuracy, repro­du­ci­bility, and develo­pment lifecycle automation.
• Benchmark and syste­ma­ti­cally test in-house and public methods with experi­mental confir­mation data
• Build database and predictive AI systems for the discovery of novel therapy targets and biomarkers
• Provide guidance and support to PhD students and scien­tists on best practices in repro­du­cible data science and high perfor­mance compute workflows
• Colla­borate closely with multi­di­sci­plinary teams of developers, techni­cians, scien­tists, and PhD students across multiple projects

What you bring:

• MSc in Bioin­for­matics, Computer Science, or a related field
• At least two years of profes­sional experience in bioin­for­matics software develo­pment
• Advanced programming skills in Python and Nextflow; experience with R and Snakemake or other workflow languages is a plus
• Profi­ciency in struc­tured software develo­pment practices, including version control, testing, contai­ne­rization, and CI/CD systems
• Hands-on experience with Linux-based compute clusters, job schedulers, and cloud computing
• Familiarity with next-generation sequencing (NGS) data and related bioin­for­matics tools is advan­ta­geous
• Knowledge of databases and machine learning libraries is a plus

Enthu­siasm and curiosity for the diverse activities of our research institute complete your profile.

We offer:

• A dynamic, innovative, and creative research environment
• An open, collegial, and supportive working atmosphere in a respectful organiza­tional culture
• A highly diverse and inclusive workforce
• Access to our GPU-accele­rated HPC cluster and labora­tories with cutting-edge sequencing techno­logies
• Perfor­­­mance-based remune­ration and other benefits
• Oppor­tu­nities for perso­na­lized profes­sional develo­pment
• Conve­nient access via public transport and car as well as bicycle parking spaces
• The possi­bility of hybrid working arran­ge­ments

TRON is an inter­na­tio­nally recog­nised institute for appli­­­­­cation-oriented research. We combine the strengths of academic research with the requi­re­ments of quality-controlled indus­trial develo­p­ments. At TRON, we share a common mission to develop innovative solutions for the immuno­the­ra­peutic treatment of cancer, infec­tious diseases and other serious diseases with high medicinal need for develo­pment.

TRON was founded in Mainz in 2010 and works in close coope­ration with univer­sities and hospitals as well as with regional, national and inter­na­tional research insti­tu­tions and pharmaceu­tical companies.

As part of our team, you will have the oppor­tunity to work at the cutting edge of trans­la­tional science.

If all this appeals to you, we look forward to getting to know you.

Please send us your complete and infor­mative appli­cation documents (cover letter, CV, references) in a single document of max. 5 MB by e‑mail to Human Resources at jobs (at) tron-mainz.de,
Job-ID: 43104–25–01-WAMSC.

For more infor­mation, visit our homepage at www.tron-mainz.de and our GitHub page: https://github.com/TRON-Bioinformatics