Merge branch 'master' of github.com:NaoMod/naomod.github.io

members.html

@@ -41,7 +41,7 @@
     title: Associate Professor
     affiliation: Nantes University
     picture: jmm.jpg
-    website: http://pagesperso.lina.univ-nantes.fr/~mottu-jm/welcome-en.html
+    website: https://pagesperso.ls2n.fr/~mottu-jm/welcome-en.html
 
   - name: Dalila Tamzalit
     title: Associate Professor
@@ -53,9 +53,14 @@
 
   - name: Hiba Ajabri
     title: Ph.D. Student
-    affiliation: Co-supervision with VELO and CPS3 teams
+    affiliation: Co-supervision with VELO and LabSTICC
     picture: hiba.png
 
+  - name: Hiba Awad
+    title: Ph.D. Student
+    affiliation: Co-supervision with STACK team
+    picture: hiba-awad.jpg
+
   - name: Ali Benjilany
     title: Ph.D. Student
     affiliation: Co-supervision with VELO team
@@ -78,6 +83,10 @@
     title: Ph.D. Student
     picture: james.png
     website: https://jameswpm.github.io/personal/
+
+  - name: Zakaria Hachm
+    title: Ph.D. Student
+    picture: zakaria.jpg
 
 former:
 

projects.html

@@ -8,16 +8,30 @@
 
 projects:
 
+  - title: MATISSE
+    website: https://www.linkedin.com/company/matisse-project-kdt
+    funder: Horizon KDT JU - Europe
+    type: International funded project
+    start_date: September 2024
+    end_date: August 2027
+    desc: |
+      MATISSE creates a framework incorporating methods and tools for 
+      the efficient and continuous engineering and validation of industrial 
+      systems. This is supported by Digital Twins leveraging the advantages 
+      of model-based, data-driven (including AI/ML), and cloud techniques to 
+      enable validation and verification services that improve productivity and 
+      quality significantly.
+
   - title: AIDOaRt
     website: https://www.aidoart.eu/
     funder: H2020 ECSEL JU - Europe
     type: International funded project
     start_date: April 2021
     end_date: September 2024
     desc: |
-      Following-up the MegaM@Rt project (cf. below), AIDOaRt will investigate
-      the AI-augmented automation of software engineering activities such as 
-      modeling, coding, testing and monitoring in order to address the 
+      Following-up the MegaM@Rt project (cf. below), AIDOaRt investigated
+      the AI-augmented automation of software and systems engineering activities 
+      such as modeling, coding, testing and monitoring in order to address the 
       challenge of the continuous development of large-scale industrial 
       Cyber-Physical Systems (CPSs).
 
@@ -45,11 +59,11 @@
       The overall approach of MegaM@Rt is to scale up the use of model-based
       techniques by offering proper methods and related tooling intera cting
       between both design time and runtime, as well as to validate the designed
-        and developed approach in concrete industrial cases involving complex 
-        systems. Thanks to the model-based method, different systems will be 
-        able to communicate through the entire development chain, and moreover 
-        it will be possible for systems in operation to feed back the collected 
-        data for continued development
+      and developed approach in concrete industrial cases involving complex 
+      systems. Thanks to the model-based method, different systems are be 
+      able to communicate through the entire development chain, and moreover 
+      it is possible for systems in operation to feed back the collected 
+      data for continued development
 
   - title: RODIC 
     website: https://rodic.ls2n.fr/
@@ -90,17 +104,6 @@
       funded through a call for collaborative innovation among SMEs in Pays de la Loire 
       and Brittany.
 
-  - title: DR3AM-ML 
-    funder: Atlanstic 2020 - France 
-    type: National funded project
-    start_date: 2021
-    end_date: 2022
-    desc: |
-      DR3AM-ML aims at facilitating the (semi-)automated identification of possible
-      microservices for the migration of already existing applications to modern 
-      (microservices-based) architectures, by combining metaheuristic optimization with 
-      Machine Learning techniques.
-
 ---
 
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