diff --git a/qml_essentials/model.py b/qml_essentials/model.py
index 5acbb2d..8926588 100644
--- a/qml_essentials/model.py
+++ b/qml_essentials/model.py
@@ -88,54 +88,17 @@ def __init__(
             self.degree = 1
 
         log.info(f"Number of implicit layers set to {impl_n_layers}.")
-
-        params_shape: Tuple[int, int] = (
+        # calculate the shape of the parameter vector here, we will re-use this in init.
+        self._params_shape: Tuple[int, int] = (
             impl_n_layers,
             self.pqc.n_params_per_layer(self.n_qubits),
         )
+        # this will also be re-used in the init method,
+        # however, only if nothing is provided
+        self._inialization_strategy = initialization
 
-        def set_control_params(params: np.ndarray, value: float) -> np.ndarray:
-            indices = self.pqc.get_control_indices(self.n_qubits)
-            if indices is None:
-                warnings.warn(
-                    f"Specified {initialization} but circuit\
-                    does not contain controlled rotation gates.\
-                    Parameters are intialized randomly.",
-                    UserWarning,
-                )
-            else:
-                params[:, indices[0] : indices[1] : indices[2]] = (
-                    np.ones_like(params[:, indices[0] : indices[1] : indices[2]])
-                    * value
-                )
-            return params
-
-        rng = np.random.default_rng(random_seed)
-        if initialization == "random":
-            self.params: np.ndarray = rng.uniform(
-                0, 2 * np.pi, params_shape, requires_grad=True
-            )
-        elif initialization == "zeros":
-            self.params: np.ndarray = np.zeros(params_shape, requires_grad=True)
-        elif initialization == "pi":
-            self.params: np.ndarray = np.ones(params_shape, requires_grad=True) * np.pi
-        elif initialization == "zero-controlled":
-            self.params: np.ndarray = rng.uniform(
-                0, 2 * np.pi, params_shape, requires_grad=True
-            )
-            self.params = set_control_params(self.params, 0)
-        elif initialization == "pi-controlled":
-            self.params: np.ndarray = rng.uniform(
-                0, 2 * np.pi, params_shape, requires_grad=True
-            )
-            self.params = set_control_params(self.params, np.pi)
-        else:
-            raise Exception("Invalid initialization method")
-
-        log.info(
-            f"Initialized parameters with shape {self.params.shape}\
-            using strategy {initialization}."
-        )
+        # ..here! where we only require a seed
+        self.initialize_params(random_seed)
 
         # Initialize two circuits, one with the default device and
         # one with the mixed device
@@ -238,6 +201,55 @@ def shots(self, value: Optional[int]) -> None:
             value = None
         self._shots = value
 
+    def initialize_params(self, random_seed, initialization: str = None) -> None:
+        # use existing strategy if not specified
+        initialization = initialization or self._inialization_strategy
+
+        def set_control_params(params: np.ndarray, value: float) -> np.ndarray:
+            indices = self.pqc.get_control_indices(self.n_qubits)
+            if indices is None:
+                warnings.warn(
+                    f"Specified {initialization} but circuit\
+                    does not contain controlled rotation gates.\
+                    Parameters are intialized randomly.",
+                    UserWarning,
+                )
+            else:
+                params[:, indices[0] : indices[1] : indices[2]] = (
+                    np.ones_like(params[:, indices[0] : indices[1] : indices[2]])
+                    * value
+                )
+            return params
+
+        rng = np.random.default_rng(random_seed)
+        if initialization == "random":
+            self.params: np.ndarray = rng.uniform(
+                0, 2 * np.pi, self._params_shape, requires_grad=True
+            )
+        elif initialization == "zeros":
+            self.params: np.ndarray = np.zeros(self._params_shape, requires_grad=True)
+        elif initialization == "pi":
+            self.params: np.ndarray = (
+                np.ones(self._params_shape, requires_grad=True) * np.pi
+            )
+        elif initialization == "zero-controlled":
+            self.params: np.ndarray = rng.uniform(
+                0, 2 * np.pi, self._params_shape, requires_grad=True
+            )
+            self.params = set_control_params(self.params, 0)
+        elif initialization == "pi-controlled":
+            self.params: np.ndarray = rng.uniform(
+                0, 2 * np.pi, self._params_shape, requires_grad=True
+            )
+            self.params = set_control_params(self.params, np.pi)
+        else:
+            raise Exception("Invalid initialization method")
+
+        log.info(
+            f"Initialized parameters with shape {self.params.shape}\
+            using strategy {initialization}."
+        )
+
     def _iec(
         self,
         inputs: np.ndarray,