Θέσεις εργασίας για αποφοίτους ή τελειόφοιτους ΗΜΜΥ

Θέματα που αφορούν το τμήμα των Η.Μ.Μ.Υ.

Re: Θέσεις εργασίας για αποφοίτους ή τελειόφοιτους ΗΜΜΥ

Δημοσίευσηαπό mitia98 » 27 Ιαν 2020, 19:55

2 διαθέσιμες Θέσεις Υποψήφιων Διδακτόρων ΗΜΜΥ στο Πολυτεχνείο Κρήτης



Στα πλαίσια του έργου AMPERE (Batteryless, Ambiently-Powered Internet of Things That Think: An Asynchronous Message Passing Approach), που υλοποιείται στο Πολυτεχνείο Κρήτης (Χανιά) (www.tuc.gr), με φορέα το Ερευνητικό Πανεπιστημιακό Ινστιτούτο Τηλεπικοινωνιακών Συστημάτων (www.tsi.gr) και επιστημονικό υπεύθυνο τον καθ. Άγγελο Μπλέτσα, υπάρχουν διαθέσιμες 2 αμειβόμενες θέσεις για υποψήφιους διδάκτορες.



Οι υποψήφιοι θα εκπονήσουν διδακτορική διατριβή στην Σχολή Ηλεκτρολόγων Μηχανικών & Μηχανικών Υπολογιστών (ΗΜΜΥ) Πολυτεχνείου Κρήτης, Χανιά (www.ece.tuc.gr). Η Σχολή προσφέρει πρόσβαση σε κατάλογο οργανωμένων μεταπτυχιακών μαθημάτων και σε αναπτυγμένο οικοσύστημα ανταγωνιστικής έρευνας και καινοτομίας.



Αμοιβή: Μηνιαίος μισθός για 36μήνες + έξοδα ταξιδιών/συμμετοχής σε συνέδρια.



Κριτήρια εισαγωγής στο Πρόγραμμα Διδακτορικών Σπουδών της Σχολής:

https://www.ece.tuc.gr/index.php?id=409 ... 967d0bce29



Θέση 1 (Software/Algorithms): πολύ καλή γνώση προγραμματισμού και μαθηματικών. Εμπειρία σε τουλάχιστον ένα από τα παρακάτω αντικείμενα: αλγόριθμοι συμπερασμού, επεξεργασία σήματος για τηλεπικοινωνίες, πιθανοθεωρία/στατιστική, βελτιστοποίηση, άλγεβρα, αντικειμενοστραφής προγραμματισμός.



Θέση 2 (Electronics/Embedded Hardware/Middleware): πολύ καλή γνώση αναλογικών ηλεκτρονικών ή προγραμματισμού ενσωματωμένων συστημάτων. Εμπειρία σε τουλάχιστον ένα από τα παρακάτω αντικείμενα: προγραμματισμός μικροελεγκτών, σχεδίαση αναλογικών φίλτρων, σχεδίαση ενισχυτών RF, σχεδίαση mixed-signal συστημάτων, προγραμματισμός αναδιατασσόμενων.



Περισσότερες Πληροφορίες: Καθ. Άγγελος Μπλέτσας ([email protected])
https://www.telecom.tuc.gr/~aggelos/



Προθεσμία Υποβολής: μέχρι πλήρωσης των θέσεων.



Batteryless, Ambiently-Powered Internet of Things That Think: An Asynchronous Message Passing Approach



Το Διαδίκτυο των Σκεπτόμενων Πραγμάτων Χωρίς Μπαταρία Με Ισχύ από το Περιβάλλον: Προσέγγιση μέσω Ασύγχρονης Ανταλλαγής Μηνυμάτων



AMPERE



Powerful message passing algorithms (e.g., sum-product, max-product) have offered concrete examples on how decision making and inference can be facilitated through communication, at carefully crafted probabilistic graphs. More importantly, recent advances on scatter radio sensor networks by the principal investigator (PI) have demonstrated feasibility of ultra-low power (in the order of 20 microWatts) and cost (in the order of some Euros), joint sensing and wireless networking, through single-transistor radio frequency (RF) front-ends and reflection radio principles. Furthermore, the PI has demonstrated energy harvesting circuits from ambient RF or bioelectric sources (plants) with record-breaking sensitivity, able to harvest ambient power, as small as 1 microWatt.



AMPERE is inspired by the fact that ambient energy, e.g., solar, kinetic, thermal, bioelectric or RF, has a common characteristic: fixed (on average) density per squared (or cubic) centimetre and thus, wireless sensor networks (WSN) over an extended area (or volume) could in principle harvest a large amount of energy. Thus, autonomous, in-network decisions should be possible, solely using ambient power,
1) by exploiting ultra-low power wireless communication principles (e.g., scatter radio) and novel energy harvesting circuits, and more importantly,

2) by balancing the WSN computation and communication load of (inherently parallel and distributed) asynchronous message passing algorithms (for inference), across various (distributed in space) WSN nodes.



AMPERE offers a methodology framework for reliable inference from unreliable, ambiently-powered WSNs, with bounded execution time, quantified convergence/correctness tradeoffs, careful modifications of message passing for efficient communication, exploitation of powerful asynchronous message passing algorithms (e.g., for clustering, signal de-noising/reconstruction), as well as hidden links between message passing algorithms and iterative numerical methods. Case studies in environmental sensing / agriculture and home automation will be examined with tremendous socioeconomic impact, while the design principles should accommodate other applications.



AMPERE attempts a concrete step from coming Internet-of-Things to future Internet-of-Things-that-Think with ambient energy.
mitia98
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Δημοσιεύσεις: 32
Εγγραφή: 13 Σεπ 2010, 21:40

Re: Θέσεις εργασίας για αποφοίτους ή τελειόφοιτους ΗΜΜΥ

Δημοσίευσηαπό mitia98 » 22 Σεπ 2020, 14:50

https://pfizer.wd1.myworkdayjobs.com/en-US/PfizerCareers/job/GRC---Thessaloniki-Chortiatis/Sr-Manager-Data-Scientist_4792715-2

Sr Manager Data Scientist
GRC - Thessaloniki, Chortiatis


ROLE SUMMARY
Digital’s Science and Clinical Analytics and Informatics will partner across Pfizer Digital Technology to drive new capabilities for reuse of cross domain data and information that enables portfolio decision making and precision medicine. Digital will drive and support cross border (internal/external) data reuse, provide a hub to connect and share reuse best practices from leaders and initiatives, and identify, develop and host data and information services that reach across domains, enable reuse and build trust in R&D data and scientific information.
The Digital SCAI team connects various stakeholders to drive new capabilities for reuse of cross domain data and information that enables portfolio decision making and enhances clinical trials. This is accomplished through a focus on the secondary use of data from inside and outside the organization, analyzed by a team of data scientists with experience and/or training in health sciences to identify, describe and analyze gaps in data access, relationships, lineage or tools in response to business requests to perform new or novel information analyses that advance our portfolio or influence the external environment.
Sr manager data scientist is a very important role to advance predictive data analyses strategies, and innovative methods generating valuable insights using numerous internal and external data sources. The data scientist will provide leadership in maximizing the value of Pfizer’s investments in big data and analytics capabilities. She/he will work in a fast-paced multidisciplinary environment as the competitive landscape and data strategies keep changing rapidly.
ROLE RESPONSIBILITIES
The Sr Manager Data Scientist will:
Locate and analyze different types of relevant data from inside and outside of Pfizer to answer novel research, development, or commercial questions.
Provide leadership to and execute projects that help discover novel targets and bio markers, enhance clinical trials and reposition Pfizer’s drug products in collaboration with business partners that have diverse scientific, informatics and business management skills and experiences.
Create prototypes, predictive models and demonstrations to help articulate the value of insights for advancing the science and business
Develop Proof-of-Concept (POC) utilities for use in answering business questions with innovation, value generation and competitive edge as key differentiators; provide thought leadership and architectural know-how to scale up these utilities for mainstream use by broader scientific and business community.
Lead Business initiatives and support Pfizer Digital’s Bold Moves
Produce actionable data presentations and visualizations that are intuitive, effective and insightful for decision makers at senior levels throughout the organization
Define and execute technology POCs using cutting edge, state of the art and often unproven technologies
BASIC QUALIFICATIONS
A minimum of a bachelors’ degree with 7+ years of experience or a PhD 3+ years’ experience.
Data analysis experience with heterogeneous data types and familiarity of big data, analytics and/or data mining environments in the biological sciences.
5+ years of programming experience with one or more analytics tool (Python, SAS, R or SQL)
3+ years with a data visualization tool (e.gs., R, Spotfire, Tableau)
Machine Learning and predictive modeling skills: Ability to build and manage models to predict future outcomes using data that is often incomplete and/or imperfect and contained in disparate sources.
Statistical analysis: Ability to understand and improve possible limitations in models.
Hypothesis testing: In collaboration with scientific and business partners, able to develop hypotheses and test them with careful experiments.
Demonstrated capabilities in analyzing heterogeneous datasets; Ability to identify trends, identify outliers and find patterns
Demonstrated experience in accessing and analyzing research, clinical and/or safety data
Superior analytical skills required; Strong verbal and written communication skills
The ideal candidate would be able to work independently with the ability to prioritize activities
PREFERRED QUALIFICATIONS
Pharmaceutical industry experience preferred
Understanding of Pharmaceutical R&D data preferred
An understanding of business processes in one or more pharmaceutical domains including discovery, research, clinical, regulatory or safety would be strong plus
Experience with Jupyter Notebooks and/or Shiny applications is a plus
2+ years of data science experience with artificial Intelligence and natural language processing/textmining and/or exposure would be big plus.
Demonstrated understanding of data management principles is a plus
Experience with Electronic Medical Record and Claims data and/or public datasets a plus
Experience in the use of semantic technologies to query unstructured data and/or experience leveraging cloud-based data storage solutions are desirable.

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.
mitia98
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Δημοσιεύσεις: 32
Εγγραφή: 13 Σεπ 2010, 21:40

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