Category: Thèse

We are looking for an excellent, creative, and highly motivated scientist
with a MSc or equivalent in computer science, network science, statistical
physics, applied mathematics, data science, computational social science or
related fields. The candidate is going to work in the data analysis of
multi-attributed online social networks for understanding the emergence of
intersectional inequalities and biases in network-based algorithms. The
project is funded by a EU Horizon Europe grant (Multi-Attribute, Multimodal
Bias Mitigation in AI Systems, Grant ID: 101070285) and implemented in
close collaboration with other research organizations that are aiming for
socially responsible AI solutions.

This is a 3-year PhD position to work with Fariba Karimi [1] in the
Computational Social Science and Network Inequality team at the Complexity
Science Hub Vienna, Austria [2]. The successful candidate will play a
leading role within this funded project, designing, managing, executing,
and publishing research in collaboration with other researchers within this
project and elsewhere. You will work in close collaboration with network
scientists, physicists, computer scientists, and social scientists to
analyze multi-criteria fairness in network-based algorithms and to develop
intervention and mitigation methods.

Please send your application material to applications@csh.ac.at with the
subject line “PhD Fairness in Networks”. The application must include:

1. a curriculum vitae
2. a list of publications
3. a brief research statement (why you think you are an excellent
candidate in this topic and what research you would like to do with us)
4. any additional material that helps us to understand that you are
research-oriented, technically skilled, and creative
5. names and full contact addresses of at least two individuals who are
willing to write a letter of recommendation for you if we request it.

We will start evaluating applications immediately. The call remains open
until the position is filled. CSH is committed to the principle of equal
employment opportunity for all applicants. All employment decisions are
therefore based on job requirements, qualifications, merit, and
organizational needs. We strongly encourage individuals from
underrepresented groups to apply. We process your personal data in
accordance with the law (https://www.csh.ac.at/data-protection/).

More
info: https://www.csh.ac.at/wp-content/uploads/2022/11/CSH_Job_PhD_MULTI_CRITERIA_FAIRNESS_IN_NETWORKS.pdf

Best,
Lisette Espin-Noboa

Research and teaching position (postdoc or PhD student) in computational
linguistics
 
HTML version with hyperlinks:
https://www.coli.uni-saarland.de/~koller/page.php?id=jobs
 
 
We are looking to fill a research and teaching position in computational
linguistics at the Department of Language Science and Technology at
Saarland University. The position is part of the research group of Prof.
Alexander Koller, but offers great flexibility in developing your own
research and teaching agenda, and collaborations with other research groups
are encouraged.
 
The position is flexible with respect to topic, but it should connect
thematically with current topics of interest to the research group. These
include syntactic and semantic parsing, semantics, dialogue, and natural
language generation; themes of particular interest are currently user
adaptation in interactive NLP and neurosymbolic models. You should have
expertise in neural and/or linguistically principled methods in
computational linguistics and be willing to take an active role in shaping
the research and teaching environment of the department.
 
The position includes a teaching load of up to four hours per week in the
BSc Computational Linguistics (in German) and/or the MSc Language Science
and Technology (in English). Both programs attract excellent and highly
motivated students; it is not unusual for our students to publish papers at
peer-reviewed conferences before graduation. The MSc students in particular
are a very international crowd, with two thirds joining us from abroad. You
will typically teach two seminars per semester on topics of your choice,
which will allow you to motivate students to do BSc and MSc theses under
your supervision.
 
This is a position on the German TV-L E13 scale (100% position at the
postdoc level; 75% position at the PhD student level). The starting salary
of a 100% TV-L E13 position is a bit over 50,000 Euros per year and
increases with experience. The initial appointment will be for three years;
the position can be extended up to the limits of the German law for
academic contracts (WissZeitVG). The starting date is April 1 or later: We
want to fill the position in 2023, but could leave it open beyond April for
the ideal candidate.
 
 
Requirements
 
We are looking for candidates who have finished, or are about to complete,
an excellent PhD degree (at the postdoc level) or MSc degree (at the PhD
student level) in computational linguistics, computer science, or a related
discipline. You must be proficient in English (spoken and written); the
ability to teach in German is a plus.
 
Priority will be given to applicants at the postdoc level, who should have
demonstrated their research expertise through high-quality publications.
However, we will also consider applicants at the PhD student level.
 
 
About the department
 
Saarland University is one of the leading centers for computational
linguistics in Europe, and offers a dynamic and stimulating research
environment. The Department of Language Science and Technology consists of
about 100 research staff in nine research groups in the fields of
computational linguistics, psycholinguistics, speech processing, and corpus
linguistics.
 
The department is the centerpiece of the Collaborative Research Center 1102
“Information Density and Linguistic Encoding”. It is part of the Saarland
Informatics Campus, which brings together computer science research at the
university with world-class research institutions on campus, such as the
Max Planck Institute for Informatics, the Max Planck Institute for Software
Systems, and the German Research Center for Artificial Intelligence (DFKI).
The Saarland Informatics Campus brings together 900 researchers and 2100
students from 81 countries; SIC faculty have won 36 ERC grants.
 
Saarland University is located in Saarbrücken, a mid-sized city in the
tri-border area of Germany, France, and Luxembourg. Saarbrücken combines a
lively culture scene with a relaxed atmosphere, and is quite an affordable
place to live in. Our department maintains an international and diverse
work environment. The primary working language is English; learning German
while you are here will make it easier to connect with the local culture,
but is not necessary for your work.
 
 
How to apply
 
Please submit your application at http://apply.coli.uni-saarland.de/ak22.
Preference will be given to applications received by 22 January 2023.
 
Include a single PDF file with the following information:
 
• a statement of research interests that motivates why you are applying
for this position and outlines your research agenda;
• a full CV including your list of publications;
• scans of transcripts and academic degree certificates;
• the names, affiliations, and e-mail addresses of two people who can
provide letters of reference for you.
 
Saarland University especially welcomes applications from women and people
with disabilities.
 
If you have further questions, please email Alexander Koller <
koller@coli.uni-saarland.de>. Applications should _not_ be emailed to this
address, but submitted through the online form.

A fully-funded PhD studentship that covers 3.5 years full international
tuition, stipend of £20,668k per year, a £1k research training support per
year is available for an April or July 2023 PhD start. The studentship is
partly sponsored by National Physics Lab (NPL) in the UK and it will
involve a secondment for at least three months in the Data Science
Department of NPL during the course of the PhD.
 
The topic is on the intersection of machine learning and sequential Monte
Carlo methods and we welcome general interests on statistical machine
learning and computational statistics. We are looking for candidates who
holds a Bachelor’s degree or above in Computer Science, Electrical
Engineering, Statistics, Mathematics, Physics or similar (a First Class or
the equivalent from an overseas university) with a strong research
background on statistical machine learning, deep learning, or computational
statistics.
 
The application is rolling-based and we will fill in the position as soon
as a suitable candidate is identified. If you are interested, please get in
touch with me on yunpe…@surrey.ac.uk <http://yunpeng.li@surrey.ac.uk/> including
copies of your CV and transcripts before you apply for the position.
 
Applications should be submitted via the University of Surrey Computer
Science PhD programme
<https://www.surrey.ac.uk/postgraduate/computer-science-phd>page on the
“Apply” tab. Please clearly state the studentship title “Machine learning
meets sequential Monte Carlo” and supervisors “Dr Yunpeng Li and Prof Wenwu
Wang” on your application.

A fully funded PhD position in *Advanced Deep Data-Driven Nowcasting
Models *is available in our group at Utrecht University, The Netherlands.
(apply before *January 9th, 2023*).
 
The research in this PhD project will be on developing a new theory to
achieve more reliable and accurate nowcasting deep learning models. The key
idea is to leverage large amount of unlabelled data for learning meaningful
representation, incorporating several atmospheric variables and equipping
the models with uncertainty quantification.
 
In this context, we are looking for PhD candidates who have experience in
machine learning, deep learning and have a strong interest in the
developing novel deep learning techniques for weather elements nowcasting.
 
*For more information and guidance on how to apply, see:* PhD position in
Advanced Deep Data-Driven Nowcasting Models
<https://www.uu.nl/en/organisation/working-at-utrecht-university/jobs/phd-position-in-advanced-deep-data-driven-nowcasting-models-10-fte>

Follow this link for descriptions of PhD Studentships at the University of
East Anglia – deadline for application 22nd February 2023:
 
https://www.uea.ac.uk/web/research/research-with-us/postgraduate-research/latest-phds-and-research-studentships
 
A number of these projects have substantial computational modelling (e.g.
supervisors: Vaghi, Smith, Penny) and brain imaging (e.g. supervisors:
Vaghi, Smith, Penny, Renoult, Holmes, Coventry, Gliga, Coventry)
components.
 
Please address any queries to the relevant supervisor.
 
All the best,
 
Will Penny,
School of Psychology,
UEA.

I’m pleased to have been awarded a Queen Mary University of London Principal’s Scholarship to offer to a top quality, UK-qualified candidate to study with me and a colleague in Mathematics (Primoz Skraba).
 
We’re looking for someone who is comfortable with mathematics and also knows some combination of acoustics, audio and music.
 
This project – under the banner of Artificial Neuroscience – will look at an emergent approach to Deep Learning that applies Linear Algebra and topology to weight and activation matrices in Neural Networks. This will be studied in the context of Neural Audio topics like Music Source Separation and Enhancement, Audio Temporal-Structure Decomposition (chorus/verse, etc) and Audio Synthesis.
 
Here is a link to our web site which candidates can follow to make an application. Closing date is 31 January 2023. http://eecs.qmul.ac.uk/phd/phd-studentships/ The project description can be found here: scroll down and click on *Artificial Neuroscience*. http://eecs.qmul.ac.uk/phd/phd-studentships/principal-and-epsrc-dtp-phd-studentships-in-electronic-engineering-and-computer-science/

*Ph.D. position at University of Texas at Dallas, Data Security and Privacy
Lab*
*DEADLINE: January 1st, 2023*
 
The University of Texas at Dallas (UTD) is a rising research powerhouse
with eight schools and more than 140 academic degrees including
top-ranked programs in business, engineering, science, audiology and
arts and technology.
 
At UTD Data Security and Privacy Lab, we focus on creating technologies
that can efficiently extract useful information from any data without
sacrificing privacy or security. We have been working on security and
privacy issues raised by machine learning and AI, privacy
issues in social networks, security issues in databases, privacy issues
in health care, applied cryptography for data security, risk and
incentive issues in assured information sharing, use of data mining and
machine learning for fraud detection, botnet detection and homeland
security. Recently, we have been focusing on security, accountability
and privacy issues in machine learning, secure IoT data processing and
using blockchains for assured data sharing, and graph neural networks for
cybersecurity.
Please see a high level interview with lab director *Dr. Murat Kantarcioglu* on
the lab’s research directions.
 
https://research.utdallas.edu/blog/qa-with-dr-kantarcioglu
 
For the ongoing projects at the lab, we are looking for exceptional
Ph.D. students to join our team. Ideal candidates should have strong
knowledge in databases, data mining, machine learning and cybersecurity.
Excellent programming skills are also required. We are aware of the fact
that knowing all the subjects listed above is impossible for an
undergrad student and even very difficult for a master student.
Therefore, highly motivated students who know some of the above subjects
listed above are encouraged to apply. If you are interested in joining
our lab as Ph.D. student, please submit your CV, unofficial transcript and
additional information via the next form.
 
https://docs.google.com/forms/d/e/1FAIpQLSeTds_vdzv_Dw_fynOCEt3lOK_52IVOt-XDMlJVNdCfm29Ddw/viewform?usp=sf_link

*Ph.D. – **RESEARCH ASSOCIATE** (m/f/d)*
 
Full time, for a maximum of* 4 *years*. *The remuneration is in accordance
with *TV-L 13
<https://www.tuhh.de/t3resources/tuhh/img/universitaet/service/verwaltung/PV32/Entgelttabelle.pdf>.*
 
YOUR TASKS
 
 
– Fundamental research on topics related to the Institute for Data
Science Foundations
– Publication of research results in journals and at relevant computer
science / mathematics conferences
– Support of teaching activities and knowledge sharing tasks at the
Institute for Data Science Foundations

 

YOUR PROFILE
 
– Completed scientific university studies, in particular in the subject
area/s mathematics, computer science, or comparable
– Very good communication skills in English for publications, lectures
and project meetings
– Knowledge in relation to some of the following areas is an advantage:
o Data science
o Artificial intelligence
o Robotics o Machine learning (supervised learning, deep neural
networks, statistical learning theory, reinforcement learning)
o Mathematical background, for instance in: probability theory,
functional analysis, differential and/or algebraic geometry, information
theory, information geometry
– The following knowledge is an advantage:
o Experience in scientific writing
o Experience in Python programming

OUR OFFER
 
– We offer you the opportunity for scientific qualification with the aim
of a doctorate
– Become a member of the Institute for Data Science Foundations, and
benefit from excellent supervision and interdisciplinary exchange
– Integrate into an excellent research network with national and
international opportunities for collaboration
– Work in a motivated and interdisciplinary team, and develop your soft
skills

 

 

For further information please contact Prof. Dr. Nihat Ay, e-mail:
nihat.ay@tuhh.de or his assistant Ms. Sandra Krüger, e-mail:
sandra.krueger@tuhh.de.
 
We particularly encourage women to apply. Due to their underrepresentation,
they will be given priority in cases of equal suitability, qualifications
and professional performance.
 
Please send your complete application documents (cover letter, curriculum
vitae in table form, proof of completed training and/or university degree,
job references or certificates of employment) via the online application
system.
 
Notice for graduates of foreign educational qualifications: Please submit
proof of all obtained university degrees and, if available, the recognition
of your educational qualifications in Germany (e.g. anabin excerpts and/or
acknowledgement of previous employers).
 
We look forward to receiving your online application by* December 31st
2022.*
 
 
Position Description
<https://stellenportal.tuhh.de/jobposting/f9366b962b061a0b235d35bafd63eacccf5b0f430>
 
Please upload your application documents here
<https://stellenportal.tuhh.de/de/jobposting/f9366b962b061a0b235d35bafd63eacccf5b0f430/apply>
.
 
 
Kind regards
 
*Sandra Krüger*
 
*Assistant to Prof. Dr. **Nihat Ay*
 
*Institute for Data Science Foundations // E-21*
Hamburg University of Technology // TUHH
Hamburg Innovation Port // HIP ONE
 
Blohmstraße 15
5th floor
21079 Hamburg

Plant diseases may affect the root, steam and leaves of plants resulting in
a sizable drop of revenue for farmers as crop’s quality is affected and may
lead to food shortage and food chain disruption [1]. Traditionally, a crop
disease can be detected by visual inspection which can be a tedious
enterprise which is time and effort consuming, and errors prone. Farming
has developed extensively in the last few decades taking advantages from
developments in chemistry, physics, sensing technology, data processing and
analytics, artificial intelligence and IoT [1,3-4]. The demand for mobile
portable applications in agriculture has increased as portable technology
ubiquitousness allows for a wider deployment and a better
cost-effectiveness. With the technology, farmers can identify and detect
early infections and diseases and hence mitigate their impact, improve
treatments outcome and can prevent further infections from re-occurring.
Portable spectroscopy can be used to detect the presence of diseases on
leaves and categorise healthy plants from unhealthy ones. Such a technology
has found use in many agro-food applications as it offers short processing
times, cost-effectiveness, portability and ease-of-deployment [2,5].

Spectroscopy is the analysis of matter and its interaction with
electromagnetic radiations; and a spectral signature is the variation of
reflectance or emittance of a material with respect to wavelengths. It is a
non-destructive way to find the fingerprints of components; and hence is a
suitable method to inspect plants’ samples.

Reflectance is a measure of electromagnetic energy that bounces back from
the surface of a material; and the leaf reflectance in the visible and
near-infrared ranges are influenced by a variety of interactions (including
leaf surface and water content) which can lead to a suitable use in
classification and detection. Further, green vegetation spectral signatures
can show pigmentation in plant tissues as Chlorophyll growth is affected.
Hence it can be used for anomaly detection in remote sensing applications.
Counting the number of insects of various species is important for planning
pest control, and for guiding agricultural policy. Computer vision
algorithms can be trained with the captured footage to detect the soil
conditions, analyse the aerial view of the overall agricultural land, and
assess crop health information. Computer vision-enabled machines can be
used in sorting and grading the harvest; while automating such tasks can
offer efficiency [2,3].

Hyperspectral imaging in agriculture can significantly extend the range of
farming issues that can be addressed using remote sensing. Almost every
farming issue (weeds, diseases, etc.) changes the physiology of plants, and
therefore affects its reflective properties. Healthy and unhealthy crops
reflect the sun light differently which renders it possible to detect such
changes in the physiology of the plants and correlate them with spectra of
reflected light.

Hence the objectives of this research proposal are:

To address the complexity of crop disease monitoring and detection in the
context of smart farming taking account of different data types.

To develop a solution that integrates both computer vision and spectroscopy
related information.

To design an AI based system for classification of diseases and anomaly
detections.
Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class
Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent
qualifications, for example, a Lower Second Class Honours Degree plus a
Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of
evidence from applicants who have appropriate professional experience which
is equivalent to the learning outcomes of an Honours degree in lieu of
academic qualifications.
Desirable Criteria

If the University receives a large number of applicants for the project,
the following desirable criteria may be applied to shortlist applicants for
interview.

– First Class Honours (1st) Degree
– Masters at 70%
– Experience using research methods or other approaches relevant to the
subject domain
– Work experience relevant to the proposed project
– Publications – peer-reviewed


for further information, please
visit https://www.ulster.ac.uk/doctoralcollege/find-a-phd/1455586

There are 2 new PhD vacancies in the field of speech- and text
anonymization in the medical domain in Berlin, Germany.
 
Both positions are in the “Medinym” project, where the department of Speech
and Language Technologies of German Research Center for Artificial
Intelligence works jointly with the Quality and Usability Labs of Berlin
Institute of Technology.
 
In both departments, we’re looking for a Researcher or Junior Researcher
level, offer a 2 years contract with optional prolongation and PhD
perspective.
 
*Application deadline: Dec 23, 2022*
 
 
More details and links:
 
DFKI:
https://jobs.dfki.de/en/internal/vacancy/en-researcher-m-w-d-in-506968.html
 
TU Berlin: https://tub.stellenticket.de/de/offers/157309/?locale=en
 

 
Please circulate upon potentially interested. In case of questions pls
contact me, I’m happy to help and forward you with respect to both
vacancies.